The document discusses building a state-of-the-art RSS auditory aggregator to monitor real-time stock information using web technologies for information exchange, sonification of stock data, and binaural technology for spatialization. It describes using text-to-speech, data sonification techniques, and binaural rendering of audio signals to develop the system. The document provides examples of setting up the system to maximize user perception and aid in recognizing stock trends using different parameters. It evaluates that the RSS-feed sonification approach achieves adequate performance and that a new spatially shaped auditory display can effectively monitor RSS stock market data when using earcons.
Sound follows function // Sound communication and the relevance of timbre.audity
油
In many domains, e.g. industrial sound design or audio-branding, designers look for sounds to communicate certain values and to convey information. As computer displays get smaller on devices such as mobile phones and personal digital assistants, sound will become even more important for providing information to users. Sound can enrich a users information awareness. A better understanding of the relation between the physical characteristics (acoustics) of a sound and its perceived emotional/affective qualities (aesthetics) as well as its attributed function/meaning (semiotics and semantics), will improve creation and selection of appropriate audio content. An explorative study using auditory icons, auditory symbols (earcons) and a combination of the both (auditory symcons) was carried out to shed more light on acoustic communication with non-speech sounds. The study reveals amongst others, that further investigation on the acoustical parameter timbre is required. Thus, an ongoing study that addresses the perception of timbre is presented.
Audio Signal Identification and Search Approach for Minimizing the Search Tim...aciijournal
油
Audio or music fingerprints can be utilize to implement an economical music identification system on a
million-song library, however the system needs great deal of memory to carry the fingerprints and indexes.
Therefore, for a large-scale audio library, memory imposes a restriction on the speed of music
identifications. So, we propose an efficient music identification system which used a kind of space-saving
audio fingerprints. For saving space, original finger representations are sub-sample and only one quarters
of the original data is reserved. In this approach, memory demand is far reduced and therefore the search
speed is criticalincreasing whereas the lustiness and dependability ar well preserved. Mapping
audio information to time and frequency domain for the classification, retrieval or identification tasks
presents four principal challenges. The dimension of the input should be considerably reduced;
the ensuing options should be strong to possible distortions of the input; the feature should be informative
for the task at hand simple. We propose distortion free system which fulfils all four of these requirements.
Extensive study has been done to compare our system with the already existing ones, and the results show
that our system requires less memory, provides fast results and achieves comparable accuracy for a largescale database.
KEYWORDS
AUDIO SIGNAL IDENTIFICATION AND SEARCH APPROACH FOR MINIMIZING THE SEARCH TIM...aciijournal
油
This document describes an approach for improving the speed of audio fingerprint searches in large audio databases. It proposes using a more compact representation of audio fingerprints that reduces the memory requirements, while still maintaining accuracy. The key steps are: 1) extracting fingerprints from audio clips by transforming them into spectrograms and filtering specific frequency bands, 2) further compressing the fingerprints using wavelet decomposition and selecting the most informative components, and 3) indexing the compressed fingerprints using min-hash to allow fast retrieval of similar fingerprints from the database. The approach aims to significantly reduce search time compared to existing audio fingerprinting systems, while achieving comparable accuracy.
Audio Signal Identification and Search Approach for Minimizing the Search Tim...aciijournal
油
Audio or music fingerprints can be utilize to implement an economical music identification system on a
million-song library, however the system needs great deal of memory to carry the fingerprints and indexes.
Therefore, for a large-scale audio library, memory imposes a restriction on the speed of music
identifications. So, we propose an efficient music identification system which used a kind of space-saving
audio fingerprints. For saving space, original finger representations are sub-sample and only one quarters
of the original data is reserved. In this approach, memory demand is far reduced and therefore the search
speed is criticalincreasing whereas the lustiness and dependability ar well preserved. Mapping
audio information to time and frequency domain for the classification, retrieval or identification tasks
presents four principal challenges. The dimension of the input should be considerably reduced;
the ensuing options should be strong to possible distortions of the input; the feature should be informative
for the task at hand simple. We propose distortion free system which fulfils all four of these requirements.
Extensive study has been done to compare our system with the already existing ones, and the results show
that our system requires less memory, provides fast results and achieves comparable accuracy for a largescale database.
Audio Signal Identification and Search Approach for Minimizing the Search Tim...aciijournal
油
Audio or music fingerprints can be utilize to implement an economical music identification system on a
million-song library, however the system needs great deal of memory to carry the fingerprints and indexes.
Therefore, for a large-scale audio library, memory
imposes a restriction on the speed of music
identifications. So, we propose an efficient music
identification system which used a kind of space-saving
audio fingerprints. For saving space, original finger representations are sub-sample and only one quarters
of the original data is reserved. In this approach,
memory demand is far reduced and therefore the search
speed is criticalincreasing whereas the lustiness and dependability are well preserved. Mapping
audio information to time and frequency domain for
the classification, retrieval or identification tasks
presents four principal challenges. The dimension o
f the input should be considerably reduced;
the ensuing options should be strong to possible distortions of the input; the feature should be informative
for the task at hand simple. We propose distortion
free system which fulfils all four of these requirements.
Extensive study has been done to compare our system
with the already existing ones, and the results sh
ow
that our system requires less memory, provides fast
results and achieves comparable accuracy for a large-
scale database.
Streaming Audio Using MPEG7 Audio Spectrum Envelope to Enable Self-similarit...TELKOMNIKA JOURNAL
油
The ability of traditional packet level Forward Error Correction approaches can limit errors for
small sporadic network losses but when dropouts of large portions occur listening quality becomes an
issue. Services such as audio-on-demand drastically increase the loads on networks therefore new, robust
and highly efficient coding algorithms are necessary. One method overlooked to date, which can work
alongside existing audio compression schemes, is that which takes account of the semantics and natural
repetition of music through meta-data tagging. Similarity detection within polyphonic audio has presented
problematic challenges within the field of Music Information Retrieval. We present a system which works
at the content level thus rendering it applicable in existing streaming services. Using the MPEG7 Audio
Spectrum Envelope (ASE) gives features for extraction and combined with k-means clustering enables
self-similarity to be performed within polyphonic audio.
This document discusses the use of artificial intelligence in organized sound as surveyed in the journal Organised Sound. It provides an overview of key AI technologies like Auto-Tune audio processing that can correct pitch and organize sound. Applications discussed include general sound classification, open sound control for music networking, and time-frequency representations for sound analysis and resynthesis. The document also outlines recent research on intelligent composer assistants, responsive instruments, and recognition of musical sounds. Finally, it discusses the future of AI in organizing sound through planning and machine learning.
Future Proof Surround Sound Mixing using AmbisonicsBruce Wiggins
油
1. Ambisonics allows for audio mixing and playback that is independent of speaker configuration, allowing a single mix to be played on various speaker arrays from 2 to 24 speakers.
2. B-format encoding with 4 channels (W, X, Y, Z) represents soundfields in a spherical harmonic basis and can be decoded to any speaker layout.
3. Recent free software plugins and file formats now make it practical to create, distribute and playback ambisonic audio mixes.
SnormA Prototype for Increasing Audio File Stepwise NormalizationIJERA Editor
油
This paper introduces a novel concept SNORM (Step NORMalization) for increasing normalization. It is a
prototype algorithm for increasing normalization based on loudness factor of the audio. The function effect
normalization plays a vital role in loudness control. The proposed experiment carried out for increasing
normalization based on step wise increase yield the variations in peaks of the audio file. The experimental
results are shown in the form of graphical analysis of the plot spectrum values of frequency analysis. From the
results, it is clearly apparent that, the normalization values are increased at different levels. The function
SNORM can set a new benchmark in the field of audio industry for the processes of increasing normalization.
SNORM can be substantial in the audio broadcast systems for applications in live audio streaming, news
broadcast, sports coverage, live programming where the loudness control mechanism is essential. For the
selective or predictive loudness control systems SNORM can be effectively applied.
Speech recognition technology allows users to communicate through spoken commands. It works by converting acoustic speech signals captured by a microphone into text. There are two main types of speech models - speaker independent models that can recognize many people, and speaker dependent models customized for a single person. The speech recognition process involves an audio input being digitized, then broken down into phonemes which are statistically modeled and matched to words in a grammar according to a dictionary to output recognized text.
Optimized audio classification and segmentation algorithm by using ensemble m...Venkat Projects
油
The document proposes an optimized audio classification and segmentation algorithm that segments audio streams into four types - pure speech, music, environment sound, and silence - using ensemble methods. It uses a hybrid classification approach of bagged support vector machines and artificial neural networks. The algorithm aims to accurately segment audio with minimum misclassification and requires less training data, making it suitable for real-time applications. It segments non-speech portions into music or environment sound and further divides speech into silence and pure speech. The algorithm achieves approximately 98% accurate segmentation.
The document discusses audio mining, which uses speech recognition technology to analyze digitized audio content like newscasts and meetings and create searchable indexes. It describes two main approaches: text-based indexing that converts speech to text, and phoneme-based indexing that works with sounds instead of text. Several challenges of audio mining are discussed, such as improving precision for applications like medical transcription. Potential uses of audio mining include analyzing customer service calls and intercepted phone conversations.
Knn a machine learning approach to recognize a musical instrumentIJARIIT
油
An outline is provided of a proposed system to recognize musical instruments using machine learning techniques. The system first extracts features from audio files using the MIR toolbox in Matlab. It then uses a hybrid feature selection method and vector quantization to identify instruments. Specifically, the key audio descriptors are selected and feature vectors are generated and matched to standard vectors to classify the instrument. The k-nearest neighbors algorithm is used for classification. Preliminary results show the system can accurately recognize instruments based on extracted acoustic features.
survey on Hybrid recommendation mechanism to get effective ranking results fo...Suraj Ligade
油
These days clients are having exclusive
requirements towards advancements, they need to hunt tunes
in such circumstances where they are not ready to recall tunes
title or melody related points of interest. Recovery of music or
melodies substance is one of the hardest errands and testing
work in the field of Music Information Retrieval (MIR). There
are different looking techniques created and executed, yet
these seeking strategies are no more ready to inquiry tunes
which required by the clients and confronting different issues
like programmed playlist creation, music suggestion or music
pursuit are connected issues. In past framework client seek
the tune with the assistance of tune title, craftsman name and
whatever other related points of interest so this strategy is
exceptionally tedious. To beat this issue singing so as to look
tune or murmuring a segment of it is the most regular
approach to seek the tune. This hunt strategy is the most
helpful when client don't have entry to sound gadget or client
can't review the traits of the tune such as tune title, name of
craftsman, name of collection. In proposed framework client
have not stress over recalling the tune data and this technique
is not tedious. In this strategy we utilize the data from a
client's hunt history and in addition the normal properties of
client's comparative foundations. Cross breed proposal
component utilizes the substance construct recovery
framework situated in light of utilization of the sound data
such as tone, pitch, mood. This component used to get exact
result to the client. The more imperative idea is clients ready
to work their gadgets without manual information orders by
hand. It is simple and basic system to perform music look.
Query By Humming - Music Retrieval TechniqueShital Kat
油
This seminar report summarizes query by humming technology. The basic architecture involves extracting melodic information from a hummed input, transcribing it, and comparing it to melodic contours in a database. Challenges include imperfect user queries and accurately capturing pitches from hums. Popular query by humming applications include Shazam, SoundHound, and Midomi. The report also discusses file formats like WAV and MIDI, and the Parsons code algorithm for representing melodies.
This document provides a glossary of terms related to sound design and production for computer games. It defines terms such as foley artistry, sound libraries, file formats like .wav and .aiff, compression types, audio hardware limitations, and audio configurations like mono, stereo, and surround sound. For each term, it provides a short definition and links to external sources, as well as describing the relevance of the term to the document author's own production practice. The glossary is intended to research and gather definitions for provided terms as part of a BTEC course assignment on sound design for computer games.
This document provides an outline and details of a student internship project on text-to-speech conversion using the Python programming language. The project was conducted at iPEC Solutions, which provides AI training and services. The student designed a text-to-speech system using tools including Praat, Audacity, and WaveSurfer. The system converts text to speech by extracting phonetic components, matching them to inventory items, and generating acoustic signals for output. The project aimed to help those with communication difficulties through improved accessibility of text-to-speech technology.
The document discusses automatic metadata generation of audio streams using audio mining techniques from Fraunhofer IAIS. It provides an overview of Fraunhofer as the largest applied research organization in Europe, and their work in areas like speech recognition, audio mining, and media archiving. The presentation describes Fraunhofer's audio mining solution and technologies for structuring audio content, including speaker diarization, speech recognition, and keyword generation.
ACHIEVING SECURITY VIA SPEECH RECOGNITIONijistjournal
油
Speech is one of the essential sources of the conversation between human beings. We as humans speak and listen to each other in human-human interface. People have tried to develop systems that can listen and prepare a speech as persons do so naturally. This paper presents a brief survey on Speech recognition, allow people to compose documents and control their computers with their voice. In other words, the process of enabling a machine (like a computer) to identify and respond to the sounds produced in human speech. ASR can be treated as the independent, computer-driven script of spoken language into readable text in real time. The Speech Recognition system requires careful attention to the following issues: Meaning of various types of speeches, speech representation, feature extraction techniques, speech classifiers, and database and performance evaluation. This paper helps in understanding the technique along with their pros and cons. A comparative study of different technique is done as per stages.
Kyle Fielding produced a glossary of terms related to sound design and production for a games design course. The glossary contains definitions for terms like Foley artistry, sound libraries, audio file formats like .wav and .aiff, lossy compression formats like .mp3, audio hardware limitations such as sound processor units and digital sound processors, and audio techniques including mono, stereo, and surround sound. Kyle explained how each term is relevant to his own production practice, such as using sound libraries to organize sounds and common file formats when saving and opening files.
Development of Algorithm for Voice Operated Switch for Digital Audio Control ...IJMER
油
International Journal of Modern Engineering Research (IJMER) is Peer reviewed, online Journal. It serves as an international archival forum of scholarly research related to engineering and science education.
The document discusses speech recognition and voice recognition. It covers what voice is, the components of sound, why voices are different, classification of speech sounds, the speech production process, what voice recognition is, automatic speech recognition (ASR), types of ASR systems including speaker-dependent and speaker-independent, approaches to speech recognition including template matching and statistical approaches, and the process of speech recognition.
The primary goal of this paper is to provide an overview of existing Text-To-Speech (TTS) Techniques by highlighting its usage and advantage. First Generation Techniques includes Formant Synthesis and Articulatory Synthesis. Formant Synthesis works by using individually controllable formant filters, which can be set to produce accurate estimations of the vocal-track transfer function. Articulatory Synthesis produces speech by direct modeling of Human articulator behavior. Second Generation Techniques incorporates Concatenative synthesis and Sinusoidal synthesis. Concatenative synthesis generates speech output by concatenating the segments of recorded speech. Generally, Concatenative synthesis generates the natural sounding synthesized speech. Sinusoidal Synthesis use a harmonic model and decompose each frame into a set of harmonics of an estimated fundamental frequency. The model parameters are the amplitudes and periods of the harmonics. With these, the value of the fundamental can be changed while keeping the same basic spectral..In adding, Third Generation includes Hidden Markov Model (HMM) and Unit Selection Synthesis.HMM trains the parameter module and produce high quality Speech. Finally, Unit Selection operates by selecting the best sequence of units from a large speech database which matches the specification.
MLConf2013: Teaching Computer to Listen to MusicEric Battenberg
油
The document discusses machine listening and music information retrieval. It introduces common techniques in music auto-tagging like extracting features from audio spectrograms and training classifiers. Deep learning approaches that learn features directly from data are showing promise. Recurrent neural networks are discussed for modeling temporal dependencies in music, with an example of applying them to onset detection. The talk concludes with an example of live drum transcription using drum modeling, onset detection, spectrogram slicing and non-negative source separation.
The document provides an overview of Music Information Retrieval (MIR) techniques for analyzing music with computers. It discusses common MIR tasks like genre/mood classification, beat tracking, and music similarity. Recent approaches to music auto-tagging using deep learning are highlighted, such as using neural networks to learn features directly from audio rather than relying on hand-designed features. Recurrent neural networks are presented as a way to model temporal dependencies in music for applications like onset detection. As an example, the document describes a system for live drum transcription that uses onset detection, spectrogram slicing, and non-negative matrix factorization for source separation to detect drum activations in real-time performance audio.
This document contains a glossary of terms related to sound design and production for computer games. It provides definitions for terms like Foley artistry, sound libraries, uncompressed audio formats (.wav and .aiff files), lossy compression, sound cards, digital sound processors, random access memory, mono/stereo/surround sound, analogue and digital recording systems, MIDI, software sequencers, plugins, MIDI keyboards, and constraints from bit-depth and sample rate on file size. The student has researched definitions and provided details on how each term relates to their own production practice.
Jordan Smith has produced a glossary of terms related to sound design and production for computer games. The glossary contains definitions for terms like Foley Artistry, Sound Libraries, audio file formats like .wav and .mp3, limitations like RAM and mono audio, recording systems such as CDs and MIDI, sampling constraints like bit depth and sample rate, and tools like plug-ins and MIDI keyboards. Jordan provides context for each term and how it relates to his own production work where possible.
SnormA Prototype for Increasing Audio File Stepwise NormalizationIJERA Editor
油
This paper introduces a novel concept SNORM (Step NORMalization) for increasing normalization. It is a
prototype algorithm for increasing normalization based on loudness factor of the audio. The function effect
normalization plays a vital role in loudness control. The proposed experiment carried out for increasing
normalization based on step wise increase yield the variations in peaks of the audio file. The experimental
results are shown in the form of graphical analysis of the plot spectrum values of frequency analysis. From the
results, it is clearly apparent that, the normalization values are increased at different levels. The function
SNORM can set a new benchmark in the field of audio industry for the processes of increasing normalization.
SNORM can be substantial in the audio broadcast systems for applications in live audio streaming, news
broadcast, sports coverage, live programming where the loudness control mechanism is essential. For the
selective or predictive loudness control systems SNORM can be effectively applied.
Speech recognition technology allows users to communicate through spoken commands. It works by converting acoustic speech signals captured by a microphone into text. There are two main types of speech models - speaker independent models that can recognize many people, and speaker dependent models customized for a single person. The speech recognition process involves an audio input being digitized, then broken down into phonemes which are statistically modeled and matched to words in a grammar according to a dictionary to output recognized text.
Optimized audio classification and segmentation algorithm by using ensemble m...Venkat Projects
油
The document proposes an optimized audio classification and segmentation algorithm that segments audio streams into four types - pure speech, music, environment sound, and silence - using ensemble methods. It uses a hybrid classification approach of bagged support vector machines and artificial neural networks. The algorithm aims to accurately segment audio with minimum misclassification and requires less training data, making it suitable for real-time applications. It segments non-speech portions into music or environment sound and further divides speech into silence and pure speech. The algorithm achieves approximately 98% accurate segmentation.
The document discusses audio mining, which uses speech recognition technology to analyze digitized audio content like newscasts and meetings and create searchable indexes. It describes two main approaches: text-based indexing that converts speech to text, and phoneme-based indexing that works with sounds instead of text. Several challenges of audio mining are discussed, such as improving precision for applications like medical transcription. Potential uses of audio mining include analyzing customer service calls and intercepted phone conversations.
Knn a machine learning approach to recognize a musical instrumentIJARIIT
油
An outline is provided of a proposed system to recognize musical instruments using machine learning techniques. The system first extracts features from audio files using the MIR toolbox in Matlab. It then uses a hybrid feature selection method and vector quantization to identify instruments. Specifically, the key audio descriptors are selected and feature vectors are generated and matched to standard vectors to classify the instrument. The k-nearest neighbors algorithm is used for classification. Preliminary results show the system can accurately recognize instruments based on extracted acoustic features.
survey on Hybrid recommendation mechanism to get effective ranking results fo...Suraj Ligade
油
These days clients are having exclusive
requirements towards advancements, they need to hunt tunes
in such circumstances where they are not ready to recall tunes
title or melody related points of interest. Recovery of music or
melodies substance is one of the hardest errands and testing
work in the field of Music Information Retrieval (MIR). There
are different looking techniques created and executed, yet
these seeking strategies are no more ready to inquiry tunes
which required by the clients and confronting different issues
like programmed playlist creation, music suggestion or music
pursuit are connected issues. In past framework client seek
the tune with the assistance of tune title, craftsman name and
whatever other related points of interest so this strategy is
exceptionally tedious. To beat this issue singing so as to look
tune or murmuring a segment of it is the most regular
approach to seek the tune. This hunt strategy is the most
helpful when client don't have entry to sound gadget or client
can't review the traits of the tune such as tune title, name of
craftsman, name of collection. In proposed framework client
have not stress over recalling the tune data and this technique
is not tedious. In this strategy we utilize the data from a
client's hunt history and in addition the normal properties of
client's comparative foundations. Cross breed proposal
component utilizes the substance construct recovery
framework situated in light of utilization of the sound data
such as tone, pitch, mood. This component used to get exact
result to the client. The more imperative idea is clients ready
to work their gadgets without manual information orders by
hand. It is simple and basic system to perform music look.
Query By Humming - Music Retrieval TechniqueShital Kat
油
This seminar report summarizes query by humming technology. The basic architecture involves extracting melodic information from a hummed input, transcribing it, and comparing it to melodic contours in a database. Challenges include imperfect user queries and accurately capturing pitches from hums. Popular query by humming applications include Shazam, SoundHound, and Midomi. The report also discusses file formats like WAV and MIDI, and the Parsons code algorithm for representing melodies.
This document provides a glossary of terms related to sound design and production for computer games. It defines terms such as foley artistry, sound libraries, file formats like .wav and .aiff, compression types, audio hardware limitations, and audio configurations like mono, stereo, and surround sound. For each term, it provides a short definition and links to external sources, as well as describing the relevance of the term to the document author's own production practice. The glossary is intended to research and gather definitions for provided terms as part of a BTEC course assignment on sound design for computer games.
This document provides an outline and details of a student internship project on text-to-speech conversion using the Python programming language. The project was conducted at iPEC Solutions, which provides AI training and services. The student designed a text-to-speech system using tools including Praat, Audacity, and WaveSurfer. The system converts text to speech by extracting phonetic components, matching them to inventory items, and generating acoustic signals for output. The project aimed to help those with communication difficulties through improved accessibility of text-to-speech technology.
The document discusses automatic metadata generation of audio streams using audio mining techniques from Fraunhofer IAIS. It provides an overview of Fraunhofer as the largest applied research organization in Europe, and their work in areas like speech recognition, audio mining, and media archiving. The presentation describes Fraunhofer's audio mining solution and technologies for structuring audio content, including speaker diarization, speech recognition, and keyword generation.
ACHIEVING SECURITY VIA SPEECH RECOGNITIONijistjournal
油
Speech is one of the essential sources of the conversation between human beings. We as humans speak and listen to each other in human-human interface. People have tried to develop systems that can listen and prepare a speech as persons do so naturally. This paper presents a brief survey on Speech recognition, allow people to compose documents and control their computers with their voice. In other words, the process of enabling a machine (like a computer) to identify and respond to the sounds produced in human speech. ASR can be treated as the independent, computer-driven script of spoken language into readable text in real time. The Speech Recognition system requires careful attention to the following issues: Meaning of various types of speeches, speech representation, feature extraction techniques, speech classifiers, and database and performance evaluation. This paper helps in understanding the technique along with their pros and cons. A comparative study of different technique is done as per stages.
Kyle Fielding produced a glossary of terms related to sound design and production for a games design course. The glossary contains definitions for terms like Foley artistry, sound libraries, audio file formats like .wav and .aiff, lossy compression formats like .mp3, audio hardware limitations such as sound processor units and digital sound processors, and audio techniques including mono, stereo, and surround sound. Kyle explained how each term is relevant to his own production practice, such as using sound libraries to organize sounds and common file formats when saving and opening files.
Development of Algorithm for Voice Operated Switch for Digital Audio Control ...IJMER
油
International Journal of Modern Engineering Research (IJMER) is Peer reviewed, online Journal. It serves as an international archival forum of scholarly research related to engineering and science education.
The document discusses speech recognition and voice recognition. It covers what voice is, the components of sound, why voices are different, classification of speech sounds, the speech production process, what voice recognition is, automatic speech recognition (ASR), types of ASR systems including speaker-dependent and speaker-independent, approaches to speech recognition including template matching and statistical approaches, and the process of speech recognition.
The primary goal of this paper is to provide an overview of existing Text-To-Speech (TTS) Techniques by highlighting its usage and advantage. First Generation Techniques includes Formant Synthesis and Articulatory Synthesis. Formant Synthesis works by using individually controllable formant filters, which can be set to produce accurate estimations of the vocal-track transfer function. Articulatory Synthesis produces speech by direct modeling of Human articulator behavior. Second Generation Techniques incorporates Concatenative synthesis and Sinusoidal synthesis. Concatenative synthesis generates speech output by concatenating the segments of recorded speech. Generally, Concatenative synthesis generates the natural sounding synthesized speech. Sinusoidal Synthesis use a harmonic model and decompose each frame into a set of harmonics of an estimated fundamental frequency. The model parameters are the amplitudes and periods of the harmonics. With these, the value of the fundamental can be changed while keeping the same basic spectral..In adding, Third Generation includes Hidden Markov Model (HMM) and Unit Selection Synthesis.HMM trains the parameter module and produce high quality Speech. Finally, Unit Selection operates by selecting the best sequence of units from a large speech database which matches the specification.
MLConf2013: Teaching Computer to Listen to MusicEric Battenberg
油
The document discusses machine listening and music information retrieval. It introduces common techniques in music auto-tagging like extracting features from audio spectrograms and training classifiers. Deep learning approaches that learn features directly from data are showing promise. Recurrent neural networks are discussed for modeling temporal dependencies in music, with an example of applying them to onset detection. The talk concludes with an example of live drum transcription using drum modeling, onset detection, spectrogram slicing and non-negative source separation.
The document provides an overview of Music Information Retrieval (MIR) techniques for analyzing music with computers. It discusses common MIR tasks like genre/mood classification, beat tracking, and music similarity. Recent approaches to music auto-tagging using deep learning are highlighted, such as using neural networks to learn features directly from audio rather than relying on hand-designed features. Recurrent neural networks are presented as a way to model temporal dependencies in music for applications like onset detection. As an example, the document describes a system for live drum transcription that uses onset detection, spectrogram slicing, and non-negative matrix factorization for source separation to detect drum activations in real-time performance audio.
This document contains a glossary of terms related to sound design and production for computer games. It provides definitions for terms like Foley artistry, sound libraries, uncompressed audio formats (.wav and .aiff files), lossy compression, sound cards, digital sound processors, random access memory, mono/stereo/surround sound, analogue and digital recording systems, MIDI, software sequencers, plugins, MIDI keyboards, and constraints from bit-depth and sample rate on file size. The student has researched definitions and provided details on how each term relates to their own production practice.
Jordan Smith has produced a glossary of terms related to sound design and production for computer games. The glossary contains definitions for terms like Foley Artistry, Sound Libraries, audio file formats like .wav and .mp3, limitations like RAM and mono audio, recording systems such as CDs and MIDI, sampling constraints like bit depth and sample rate, and tools like plug-ins and MIDI keyboards. Jordan provides context for each term and how it relates to his own production work where possible.
The Rise of AI Agents-From Automation to Autonomous TechnologyImpelsys Inc.
油
AI agents are more than just a buzzwordthey are transforming industries with real autonomy. Unlike traditional AI, they dont just follow commands; they think, adapt, and act independently. The future isnt just AI-enabledits AI-powered.
How AWS Encryption Key Options Impact Your Security and ComplianceChris Bingham
油
A rigorous approach to data encryption is increasingly essential for the security and compliance of all organizations, particularly here in Europe. However, all to often key management is neglected, and encryption itself aint worth much if your encryption keys are poorly managed!
AWS KMS offers a range of encryption key management approaches, each with very different impacts on both your overall information security and crucially which laws and regulations they enable compliance with.
Join this mini-webinar to learn about the choices you need to make, including:
Your options for one of the most important decisions you can make for your AWS security posture.
How your AWS KMS configuration choices can fundamentally alter your organization's regulatory compliance.
Which AWS KMS option is right for your organization.
Let's Create a GitHub Copilot Extension! - Nick Taylor, PomeriumAll Things Open
油
Presented at All Things Open AI 2025
Presented by Nick Taylor - Pomerium
Title: Let's Create a GitHub Copilot Extension!
Abstract: Get hands-on in this talk where we'll create a GitHub Copilot Extension from scratch.
We'll use the Copilot Extensions SDK, https://github.com/copilot-extensions/preview-sdk.js, and Hono.js, covering best practices like payload validation and progress notifications and error handling.
We'll also go through how to set up a dev environment for debugging, including port forwarding to expose your extension during development as well as the Node.js debugger.
By the end, we'll have a working Copilot extension that the audience can try out live.
Find more info about All Things Open:
On the web: https://www.allthingsopen.org/
Twitter: https://twitter.com/AllThingsOpen
LinkedIn: https://www.linkedin.com/company/all-things-open/
Instagram: https://www.instagram.com/allthingsopen/
Facebook: https://www.facebook.com/AllThingsOpen
Mastodon: https://mastodon.social/@allthingsopen
Threads: https://www.threads.net/@allthingsopen
Bluesky: https://bsky.app/profile/allthingsopen.bsky.social
2025 conference: https://2025.allthingsopen.org/
Columbia Weather Systems offers professional weather stations in basically three configurations for industry and government agencies worldwide: Fixed-Base or Fixed-Mount Weather Stations, Portable Weather Stations, and Vehicle-Mounted Weather Stations.
Models include all-in-one sensor configurations as well as modular environmental monitoring systems. Real-time displays include hardware console, WeatherMaster Software, and a Weather MicroServer with industrial protocols, web and app monitoring options.
Innovative Weather Monitoring: Trusted by industry and government agencies worldwide. Professional, easy-to-use monitoring options. Customized sensor configurations. One-year warranty with personal technical support. Proven reliability, innovation, and brand recognition for over 45 years.
UiPath Automation Developer Associate Training Series 2025 - Session 8DianaGray10
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In session 8, the final session of this series, you will learn about the Implementation Methodology Fundamentals and about additional self-paced study courses you will need to complete to finalize the courses and receive your credential.
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An RSS feed auditory aggregator using earcons
1. Audio Mostly : 6th Conference on Interaction with Sound Listen to your Portfolio Beat Athina Bikaki Andreas Floros
2. Which technologies we combined to build a state-of-the-art RSS auditory aggregator to monitor real-time stock information? Web technologies for information exchange . Data representation of real-time stock data through sonification . Binaural technology to achieve spatialization of information. Audio Mostly : 6th Conference on Interaction with Sound
3. What is RSS? RSS is a lightweight XML format. What does RSS look like? Why use RSS feeds? The benefits of using an RSS aggregator. Audio Mostly : 6th Conference on Interaction with Sound
4. Text-to-speech Data Sonification Audification Earcons Model-Based Sonification Parameter Mapping Sonification Binaural Rendering of audio signals Audio Mostly : 6th Conference on Interaction with Sound
5. How to pick a good stock group? Investment options How to properly sonify periodic data values? Earcons How to position the audio feeds in 3D space? Orchestration Audio Mostly : 6th Conference on Interaction with Sound
6. Build a robust RSS-feed auditory aggregator Audio Mostly : 6th Conference on Interaction with Sound
7. Take the example of oil companies group Audio Mostly : 6th Conference on Interaction with Sound
8. Audio Mostly : 6th Conference on Interaction with Sound Selection of two informational data feeds, XOM and MRO. How can we setup the system to maximize the users perception? Use of different gender voices Use of different audio source location Use of different start time
9. Selection of two numerical data feeds, XOM and MRO. Now, how can we setup the system to aid the user recognize the stock trends? Choose a different timbre (woodwinds, strings, brass) Choose a different register (low, middle, high) Use dynamics to present the volatility of the stock quote Vary the notes number and values in the measure We kept the tempo and the rhythm fixed Audio Mostly : 6th Conference on Interaction with Sound
11. Audio Mostly : 6th Conference on Interaction with Sound How to position the different sound sources in space? Choose the layout of the system (horizontal and vertical location) Make use of malefemale voices alternatively Make use of different musical instruments for earcon sonic construction and keep them in a safe distance that they do not blend Group components with similar attributes (position them in the similar direction)
13. Why the second example fails? Requires knowledge of how to use the musical parameters to setup the system. Selection of inappropriate setup spatial parameters. Little training is needed if the earcons are well designed. Audio Mostly : 6th Conference on Interaction with Sound
14. Audio Mostly : 6th Conference on Interaction with Sound The proposed RSS-feed sonification approach achieves adequate performance in terms of perceptual accuracy of the transmitted content
15. A new spatially shaped auditory display can be used to monitor RSS stock market data effectively. Earcons provide a simple, easy to memorize method to sonify simple stock data values. Binaural rendering gives flexibility to the RSS aggregator. Training on the earcons interpretation is prerequisite for the user. Audio Mostly : 6th Conference on Interaction with Sound
16. The end Audio Mostly : 6th Conference on Interaction with Sound
Editor's Notes
#2: Thanks to everyone for coming. My name is ..blah blah My educational background is MSc in Information Systems In this work, we propose a non-visual interface to monitor stock market data , both textual and numerical and this is what we call an RSS-feed auditory aggregator.
#3: Im going to discuss a little on the technologies that are evolved in the system. Firstly, we used one the most commonly used formats for information delivery, the XML and more specifically the RSS. The RSS is widely used to transmit frequently updated web content to feed readers or news aggregators. In the early days of the Internet, there was little need for different websites to communicate with each other and share data. In the new "participatory web", however, sharing data between sites has become an essential capability. To share its data with other sites, a website must be able to generate output in machine-readable formats such as XML (Atom, RSS, etc) and JSON. When a site's data is available in one of these formats, another website can use it to integrate a portion of that site's functionality into itself, linking the two together. When this design pattern is implemented, it ultimately leads to data that is both easier to find and more thoroughly categorized. Secondly, we aim to extend the concept of RSS-based information delivery and aggregation using sonification. Instead of delivering information in textual representation we propose a sonification framework to represent stock data. And lastly, we gave particular emphasis to the information representation concurrency, which is mainly achieved using sound source spatialization techniques and different timbre characteristics.
#4: RSS stands for Really Simple Syndication . It has quietly become a dominant format for distributing news headlines on the Web. It is a lightweight XML format designed for sharing headlines and other Web content. Think of it as a distributable "What's New" for your site. RSS defines an XML grammar (a set of HTML-like tags) for sharing news. Each RSS text file contains both static information about your site, plus dynamic information about your new stories, all surrounded by matching start and end tags. Each RSS channel can contain up to 15 items and is easily parsed. Say for instance that you want to monitor the latest news of some stocks belonging to your portfolio . Instead of checking the news sites every day for fresh news you now can make use of RSS, and it will automatically fetch the latest related news. Another great thing about RSS feeds is that as your interests change and the sites you follow change you can remove or add subscriptions to your feed. And also RSS is a secure channel that cant be spammed.
#5: Text-to-Speech technology, converts normal language text into speech, it synthesises text into speech. Speech synthesis has long been a vital assistive technology tool and its application in this area is significant and widespread. The technology has improved significantly in recent times, and although it does not yet duplicate the quality of recorded human speech, it is still a good option for creating messages from text that cannot be predicted, such as translating web pages for blind users. Sonification is the use of non-speech audio to convey information. Several different techniques for rendering auditory data representations can be categorized as Many different components can be altered to change the user's perception of the sound. Often, an increase or decrease in some level in this information is indicated by an increase or decrease in pitch, amplitude or tempo, but could also be indicated by varying other less commonly used components like timbre and register. We have suggested the use of text-to-speech technology and non-speech audio cues, called earcons as one way to improve the capacity of the web content transmission through parallelism and as a way of communication for general usage applications such as in-vehicle communication or visually impaired users. Binaural rendering systems is to evoke the illusion of one or more sound sources positioned around the listener using stereo headphones. The positions of the sound sources can preferably be modified in terms of the perceived azimuth, elevation, and distance. Binaural rendering has benefits in the field of research, simulation, and entertainment. Especially in the field of entertainment, the virtual auditory scene should sound very compelling and real. In order to achieve such a realistic percept, several aspects have to be taken into account, such as the change in sound source positions with respect to head movement, room acoustic properties such as early reflections and late reverberation, and using system personalization to match the anthropometric properties of the individual user.
#6: Why stock data? It incorporates multiple parallel and real-time information transmissions, allowing the evaluation of the overall functionality of the proposed system. We can use a wide range of investment options, some of them are stock quotes of a particular market group, portfolio stocks, stock indices and bonds. Earcons are brief musical melodies consisting of a few notes. They are abstract so their meaning must always be learned. Think of all the choices we have to position the audio feeds. What combinations would sound the best? There are so many choices of instruments and combinations of sounds. We focused on orchestration in order to avoid any information losses, due to concurrent RSS-feeds transmissions.
#7: The proposed sonification-enabled RSS-feed aggregator is subscribed to N different RSS-feeds. Depending on their type textual or numerical, the parsed information is organized into M information streams (where MN, a value that exclusively depends on the type of the received data). Currently, this information categorization is performed manually by the user during the initial subscription to a specific feed. The information is transmitted to the Earcon Design Engine, which is responsible for producing the appropriate earcons in real-time, taking into account the type of each information category. The binaural processing module is used to spatialize the incoming audio messages. What we achieved is to provide a robust and efficient (in terms of acoustic perception) mean of concurrency during sonification. Finally, the derived spatialized earcon signals are forwarded to the Auditory Display Synthesis module, which mixes the corresponding binaural signals and reprodes the complete auditory display.
#8: Lets assume that our portfolio consists of two stocks , the Exxon Mobil Corporation (XOM) and the Marathon Oil Corporation (MRO), both belonging to the oil companies group. We want to monitor both the daily stock data values and the latest news about these companies. Snapshots of the numerical data feeds are shown on the left, while a snapshot of the latest news feeds about these companies are on the right. We chose to monitor the % Change of a stock quote and we will see how to do this in the next slides.
#9: Lets see how to setup the speech parameters. We have selected 2 informational data feeds that we want to concurrently transmit and reproduce them in the auditory display. This is a screenshot of the speech parameterization form that we built. Firstly, we set the narrator parameters, like select the voice, the volume and the voice speed. Then we set each source localization parameters, that is, the azimuth and the vertical position. (We can personalize much more the resulting spatial audio clip by assigning our head diameter). We can also set the absolute start time of this audio clip. In the text to speak box the text that is set, was parsed and trimmed from the selected web feed.
#10: Our next step, is to setup the sonification parameters for the other 2 numerical data feeds. We assigned a different timbre (musical instrument) for each stock quote, and we used the dynamics to refer to the volatility of each stock quote. As the stock price rise, we increase the volume and as it falls we decrease the volume. We choose a different pitch or register , depending on our instrument selection. In this work we have used a single pitch on the earcon construction. Finally, we have to find a way to map the % stock value change to musical parameters that the end user can easily perceive. For this , we have used different note values and note numbers in the measure, according to a numerical scale that we built. ( Timbre is the quality of a musical note or sound or tone that distinguishes different types of sound production, such as voices and musical instruments. Register is the relative "height" or range of a note, set of pitches or pitch classes, melody, part, instrument or group of instruments Dynamics usually refers to the volume of a sound or note Tempo is the speed or pace of a given piece (beats per minute). It establishes the musical meter )
#11: This is the earcon parameterization screen where we can see an example of the information that we described in the previous slide.
#12: On this slide we are going to set up the binaural parameters of the 4 stocks that we have used in our example. We tried to choose instruments from different families (woodwinds, brass, strings), basson which belongs to the woodwinds instruments and piano which belongs either to the percussion or strings instruments (there is some debate here ) or is used for solo performances. Similar news and data are better to be positioned in the same direction (proximity), so that objects close to each other are grouped together. Also, the use of male and female voices alternatively results in a better perception. On the right we can see the visual diagram of the aforementioned example.
#13: We must understand what an instrument can and cannot do. Ranges (Middle C). Piccolo is a bad choice for the note D4. It can play well in higher register. The same is true for contrabassoon. It can play well in lower register. Besides they belong to the same family. The selection of the same 2 female voices is a bad choice , and their horizontal position is leads to further confusion to the user.
#15: Users were given a short training period time and then were presented with sounds and they had to indicate how the system was set up. Results showed that even with small amounts of training , users could get good perception rates.