It's so quiet. Let's make music.

Nov 17, 20140 likes675 views

Ruby is used for a lot of things, but for some reason, only a few people are using it for music. In a language that is meant to make programming fun, the lack of such creative code is scary. Let's fix the current landscape by learning how to use the tools available in Ruby (and some not) to let those creative juices flow. We will be focusing on how to build sounds from the ground up (the powerful amplitude, and the majestic waveform), so you don't need any prior audio wizardry. Just bring yourself and a Ruby console and we just might create some beautiful music in a beautiful language.

It��s so quiet.
Let��s make music.
Loren Segal
RubyConf 2014

Discrete Signal (not continuous)
187
198
-84 -84

Audio Vocabulary
[Sample Rate]?
��Number of data values for
each second of audio��
Measured in Hertz (Hz)

20 Samples
20.times.map {|i| (Math.sin(i) * 255).floor }
[0, 214, 231, 35, -193,
-245, -72, 167, 252, 105,
-139, -255, -137, 107, 252,
165, -74, -246, -192, 38]

Common Sample Rates
44,100 Hz
44,800 Hz
96,000 Hz
used by VHS, CDs

f(t) = A sin(2�Ц�t)
Amplitude
Frequency
s

Audio Vocabulary
[Amplitude]?
��Loudness��
Measured in Decibels (dB)

Audio Vocabulary
[Frequency]?
��Pitch��
Measured in Hertz (Hz)

f(t) = A rand()
rand() = some evenly distributed random fn

��All waveforms, no matter what you
scribble or observe in the universe,
are actually just the sum of simple
sinusoids of different frequencies.��

1.
Pitch Detection:
Instrument Tuner,
Visualization

$Write a Sine Wave require 'ruby-?�\audio' include RubyAudio sample_rate, len, freq, amp = 48000, 10, 440, 0.9 buf = Buffer.float(sample_rate * len) buf.size.times do |i| sine_rad = ((freq * Math::PI * 2) / sample_rate) * i buf[i] = amp * Math.sin(sine_rad) end format = FORMAT_WAV | FORMAT_PCM_16 info = SoundInfo.new(channels: 1, samplerate: sample_rate, format: format) snd = Sound.new('out.wav', 'w', info) snd.write(buf) snd.close$

Play a Sine Wave
require
'easy_audio'
EasyAudio.easy_open(&EasyAudio::Waveforms::SINE)
sleep
2
#
play
for
2
seconds

$Increase Frequency / Time require 'easy_audio' triangle = EasyAudio::Waveforms::TRIANGLE stream = EasyAudio.easy_open(freq: 220, &triangle) Thread.new { loop { stream.frequency += 50; sleep 0.2 } } sleep 3$

$Embedding DRb - C++ AudioEffect* createEffectInstance(audioMasterCallback audioMaster) { ruby_init(); ruby_init_loadpath(); rb_require("drb/drb"); rb_eval_string("DRb.start_service"); return new SampleVST(audioMaster); } // ... server = rb_eval_string( "DRbObject.new_with_uri('druby://localhost:9090')");$

Call Distributed Method
rb_funcall(
server,
//
the
DRb
object
rb_intern(��process��),
//
the
remote
method
1,
//
one
argument
INT2FIX(sampleFrames));
//
number
of
samples

2. Making waves!
Sine, Square,
Saw, Triangle,
Noise

4. Ruby libraries!
ruby-audio
easy_audio
easy_vst

Abstract : Although solutions to the challenge of binaural artificial recreation of audio specializations exist in the Computer Music domain, a review of the area suggests that a comprehensive, generic, accurate and efficient tool set is required; hence this paper/thesis will deal with automated sound creation using sound synthesis. The entire setup was implemented using Csound. Csound implements many synthesis techniques unavailable through other means, such as user-controlled physical modeling.

openFrameworks 007 - soundroxlu

Music as dataJohn Vlachoyiannis

This document introduces John Vlachoyiannis and discusses live programming of music using Clojure. It outlines how Clojure allows music to be treated as data that can be manipulated and transformed in real time. Examples are given showing how to define notes and samples as data, generate patterns, and manipulate those patterns by reversing, randomizing, or applying other transformations to the music structure. Live programming is enabled through use of the REPL and functions like play! that allow musical experiments to be conducted and heard immediately.

MusicEngine for Unity/ADX2LE(English)Sho Iwamoto

MusicEngine is a single MonoBehaviour script that allows games to synchronize animation and events to music. It provides functions to access the current musical timing, bar, beat and unit. Examples are provided of how to quantize audio, animate along the beat, and transition scenes based on musical timing or sections. The document also discusses differences when using MusicEngine with the Unity standalone audio system versus with ADX2LE.

Lab ActivityDwight Sabio

North pole audio affectsFreeWavesEntertainment

Matched filter detectionSURYA DEEPAK

The document describes matched filter detection using LabVIEW. It involves generating a chirp signal, adding noise to create a noisy waveform, and then using a matched filter to detect the chirp signal in the presence of noise. Specifically, it generates a chirp signal, adds white Gaussian noise, and uses a filter with an impulse response that is the time-reversed version of the chirp signal to maximize the signal-to-noise ratio and detect the chirp signal in the noisy waveform. It provides examples detecting chirp signals of different lengths in the presence of noise.

Introduction of ToySynthRansui Iso

Audio DSP in ReasonMLKen Wheeler

The document discusses digital signal processing and audio synthesis in ReasonML. It covers key concepts like sample rate, buffer size, oscillators, envelopes, and common effects like bitcrushing and delay. Code examples are provided to generate waveforms from oscillators and envelopes, as well as process audio through a bitcrusher and delay effect. The goal is to teach the fundamentals of digital audio and demonstrate how to create and manipulate sound programmatically using ReasonML.

USRP Implementation of Max-Min SNR Signal Energy based Spectrum Sensing Algor...T. E. BOGALE

Lecture13INDHULEKSHMI M.C

1) Pulse amplitude modulation (PAM) encodes digital information by varying the amplitude of a periodic pulse train based on a sampled message signal. 2) A matched filter is used at the receiver to detect pulses in the presence of noise. The matched filter is a time-reversed and scaled version of the transmitted pulse shape. 3) Intersymbol interference can occur when pulses are transmitted too closely together. Adding a guard period between pulses or using a raised cosine pulse shape can eliminate intersymbol interference.

Stream audio affectsFreeWavesEntertainment

This document discusses audio editing and mixing techniques in the REAPER audio workstation software. It explains how to organize audio tracks, add effects like volume and panning, and zoom in and out. It also describes how to use VST plugins to add dynamics processing to tracks and how the FX window can display effect options. Finally, it provides details on rendering mixdowns, including options for master mixes, stems, file naming, output format, sample rate, quality settings, and bit depth.

Lecture22nomio0703

1. The lecture covered sound waves and their properties including speed of sound in different mediums, intensity, loudness, standing waves in pipes, and the Doppler effect. 2. Honors papers are due on Friday, November 12th by email. There will be a special quiz after Thanksgiving. 3. The speed of sound is higher in helium than in air, so the fundamental frequency of a pipe would decrease if the air inside was replaced with helium.

Telecommunications: Motion VideoNapier University

This document discusses motion video encoding techniques. It explains that video frames contain redundant information between frames that compression removes. It describes the MPEG encoding process which converts frames to YUV color space, applies discrete cosine transform, quantization, and variable length coding. It also discusses I-frames, P-frames, and B-frames used in motion estimation and the differences between MPEG-2 and MPEG-4/H.264 standards.

Forgent Inc. v. High-Tech GiantsVideoguy

Forgent Inc. sued several technology giants for patent infringement over their use of the JPEG image compression standard. Forgent claimed it held a patent on a method similar to how JPEG compresses images using run-length encoding and Huffman coding. However, the Public Patent Foundation found a prior patent from 1985 that described a similar method. This prior art caused the USPTO to invalidate Forgent's patent.

Ultrasonic with buzzerJawaher Abdulwahab Fadhil

PresentazioneGiulia Cassar��

This document summarizes steps for installing and configuring the Raspberry Pi 3 Model B operating system Raspbian. It discusses downloading the Raspbian OS file, preparing the SD card, booting the system, expanding the filesystem, updating packages, installing necessary software like WiringPi and GIT, and executing demo scripts to test the keyboard and speaker functions. It also provides details on a tone function script that uses pulse width modulation to generate tones on the speaker based on a musical note frequency formula.

Sound Processing in MATLAB program speechleknasunitha

noise removal in matlabumarjamil10000

This document discusses working with sound in Matlab and Cogent. It covers loading and playing sounds, creating sounds of different frequencies and amplitudes, combining sounds, and plotting sound waves. Key steps include reading and writing .wav files, configuring sound in Cogent, generating sine waves of different frequencies to create melodies, and manipulating amplitude to make sounds louder or quieter. Functions mentioned include wavread, sound, wavwrite, config_sound, loadsound, playsound, waitsound, sin, and plot.

Fourier project presentation־�v ��

This document summarizes an audio noise cancellation project. It outlines the project schedule, methods used including decoding audio files, applying fast Fourier transforms (FFTs) and inverse FFTs (IFFTs), and developing algorithms to filter noise and find suitable noise profiles to subtract. It discusses challenges faced with data types, filter design, and implementing the inverse FFT. The overall goal is to develop techniques to remove noise from audio signals.

Vidyalankar final-essentials of communication systemsanilkurhekar

This document provides an overview of analog and digital communication systems. It discusses the basics of analog signals, frequency spectrum, and modulation. It then covers digital signals, terms, and performance metrics like data rate and bit error probability. Key concepts covered include Shannon capacity, signal energy and power, communication system blocks, filtering, and modulation. It also introduces concepts from probability theory and random processes used in analysis of communication systems like mean, autocorrelation, power spectral density, Gaussian processes, and noise. Examples of modulation techniques and noise sources in communication systems are briefly discussed.

Audio Signal Processing Ahmed A. Arefin

Noise reduction is the process of removing noise from a signal. In this project, two audio files are given: (1) speech.au and (2) noisy_speech.au. The first file contains the original speech signal and the second one contains the noisy version of the first signal. The objective of this project is to reduce the noise from the noisy file Echo and reverberation effects are used extensively in the music industry. Here we will design a digital filter that will create the echo and reverb effect on audio signals.

Polyanna manualAleksi Eeben

Polyanna is an 8-channel music editor for the Commodore 64 that allows playing 3 regular SID channels together with 5 emulated SID channels. It has two modes - an 8-channel mode with lower sound quality and a 6-channel mode with improved sound quality. The interface is easy to use for those familiar with SID music software, with regular SID channels using sounds 1-7 and emulated channels using 9-F. The document provides details on using and editing sounds, blocks, and songs as well as keyboard commands and known limitations.

The method of comparing two audio filesMinh Anh Nguyen

The document analyzes a method for comparing two audio files to detect human errors using fast Fourier transforms (FFT). It describes using FFT to convert audio files from the time domain to the frequency domain. It then calculates the mean squared error (MSE) between the normalized spectral densities of the two files. A low MSE would indicate the files are identical, while a higher MSE shows a difference. The document provides the steps and flowchart used, and includes examples of Matlab and Labview code implementing the comparison method on identical and non-identical audio files.

example based audio editingRamin Anushiravani

1) Equalizer matching involves finding the power spectrum of an example audio, then multiplying the input audio's magnitude spectrogram by a filter matching the example's power spectrum. 2) Noise matching involves denoising the input and example separately, then recombining their clean and noise components using the original signal-to-noise ratio. 3) Reverberation matching uses convolutive non-negative matrix factorization to decompose the input into a dry sound and reverb kernel, and convolve the estimated dry input with the example's reverb kernel.

Digital Tunerplun

The document discusses the design and testing of a musical instrument tuner. It covers: - How musical notes are distinguished by their frequencies in different octaves - How a tuner can identify the frequency of an input signal using a Fourier transform - The process of tuning the tuner to detect specific note frequencies like C and output high or low - Testing the tuner using a function generator and then a microphone for real instruments

The method of comparing two audio filesMinh Anh Nguyen

The document analyzes a method for comparing two audio files using the Fast Fourier Transform (FFT) algorithm to detect human errors. It describes how the FFT works, breaking signals into frames and returning frequency values. It then outlines the steps to compare audio files, including truncating signals to equal lengths, calculating normalized energy spectral density from the FFTs, and computing the mean-square-error between the signals. MATLAB and LabVIEW code examples are provided to load files, perform the FFT, calculate MSE, and display results identifying whether files are identical or not. Graphs show FFT results and cross-correlations for identical and non-identical file pairs.

Beamforming and microphone arraysRamin Anushiravani

Ramin Anushiravani's document outlines techniques for sound source localization using microphone arrays. It discusses beamforming methods like delay-and-sum and MVDR beamforming, as well as subspace-based algorithms like MUSIC. It also covers topics like uniform linear arrays, beampatterns, and spatial aliasing. The document presents results from experiments localizing 1-2 sound sources using arrays with 2-4 microphones.

Audio Data Representation (NCGD009)Adam Sporka

The document discusses audio signal representation and processing. It covers topics like: - Sound is oscillation of atmospheric pressure detectable by human ears within a range of 20-20,000 Hz. - Analog to digital conversion samples an audio signal over time and stores the values. Digital to analog conversion converts these values back to amplitudes. - Common audio data formats include WAV, FLAC for uncompressed time-domain formats, and MP3, Ogg Vorbis for compressed frequency-domain formats. - Multi-channel audio configurations have multiple speakers like stereo, 5.1 surround sound, 7.1, etc. Playback involves a sample pump that converts digital audio to analog for output.

More Related Content

What's hot (10)

Introduction of ToySynthRansui Iso

Audio DSP in ReasonMLKen Wheeler

USRP Implementation of Max-Min SNR Signal Energy based Spectrum Sensing Algor...T. E. BOGALE

Lecture13INDHULEKSHMI M.C

Stream audio affectsFreeWavesEntertainment

Lecture22nomio0703

Telecommunications: Motion VideoNapier University

Forgent Inc. v. High-Tech GiantsVideoguy

Ultrasonic with buzzerJawaher Abdulwahab Fadhil

PresentazioneGiulia Cassar��

Introduction of ToySynthRansui Iso

Audio DSP in ReasonMLKen Wheeler

USRP Implementation of Max-Min SNR Signal Energy based Spectrum Sensing Algor...T. E. BOGALE

Lecture13INDHULEKSHMI M.C

Stream audio affectsFreeWavesEntertainment

Lecture22nomio0703

Telecommunications: Motion VideoNapier University

Forgent Inc. v. High-Tech GiantsVideoguy

Ultrasonic with buzzerJawaher Abdulwahab Fadhil

PresentazioneGiulia Cassar��

Similar to It's so quiet. Let's make music. (20)

Sound Processing in MATLAB program speechleknasunitha

noise removal in matlabumarjamil10000

Fourier project presentation־�v ��

Vidyalankar final-essentials of communication systemsanilkurhekar

Audio Signal Processing Ahmed A. Arefin

Polyanna manualAleksi Eeben

The method of comparing two audio filesMinh Anh Nguyen

example based audio editingRamin Anushiravani

Digital Tunerplun

The method of comparing two audio filesMinh Anh Nguyen

Beamforming and microphone arraysRamin Anushiravani

Audio Data Representation (NCGD009)Adam Sporka

Tomas_IWAENC_keynote10.pptRakesh Pogula

This document discusses front-end audio processing techniques used in communication and recording devices. It provides an overview of classical front-end architectures used in phones from the 1990s to 2010, including filters, equalizers, encoders/decoders, volume control, acoustic echo cancellation, noise suppression, and leveling. It also discusses issues like dynamic range, quantization noise, distortion, and the tradeoffs between noise suppression and voice quality. The document reviews single-channel noise suppression techniques and metrics like convergence rate and PESQ/MOS-LQO. Finally, it introduces multi-microphone solutions like linear beamforming and differential beamforming and their benefits and limitations regarding critical distance and noise.

DJ Workshop v.0.2bMagmakern Industries

This document provides an overview of DJ hardware, software, and techniques. It covers turntables, CDJs, digital vinyl systems (DVS), controllers, and software-only systems. It discusses building an MP3 collection through listening, tagging, organizing by key, and analysis. Mixing techniques like harmonic mixing using the Camelot wheel are demonstrated. Hands-on experience is provided with Serato Scratch Live on turntables to mix tracks. Contact information is provided for booking requests.

What Shazam doesn't want you to knowRoy van Rijn

The document describes how music matching algorithms like Shazam work by: 1) Capturing an audio sample using the device microphone and extracting its frequencies using a Fourier transformation. 2) Comparing the frequency profile of the sample to profiles of reference songs that were generated in the same way. 3) Calculating offsets between matching frequency points to determine the most similar reference song.

Session3Krutarth Patel

This document discusses three Arduino projects using a piezo sounder and sensors: 1. A piezo sounder alarm that generates tones of varying frequency to create a rising and falling alarm sound. 2. A piezo knock sensor that detects knocks or touches and lights an LED. It reads the electric current generated by a piezo disc when force is applied. 3. A light sensor that uses a light dependent resistor to detect light levels and a piezo sounder to provide audible feedback. The delay between beeps decreases as more light is detected.

Audio125 wk1-review-digital-audio-specs-and-acousticsRaffiel203

This document discusses key concepts related to sound waves and digital audio. It covers topics like the parts of a sound wave, pitch and frequency, the audio range, musical pitch, wavelength, octaves, the frequency spectrum, phase, timbre, sound envelopes, acoustics, direct vs indirect sound, studio design, room acoustics, analog vs digital recordings, sampling, bit depth, quantization, AD/DA conversion, digital audio workstations, audio formats, uncompressed vs compressed files, and sample rates.

Digital signal processing through speech, hearing, and PythonMel Chua

�ݺ�ߣs from PyCon 2013 tutorial reformatted for self-study. Code at https://github.com/mchua/pycon-sigproc, original description follows: Why do pianos sound different from guitars? How can we visualize how deafness affects a child's speech? These are signal processing questions, traditionally tackled only by upper-level engineering students with MATLAB and differential equations; we're going to do it with algebra and basic Python skills. Based on a signal processing class for audiology graduate students, taught by a deaf musician.

Digital Audio & Signal Processing (Elad Gariany)Ron Reiter

This document provides an overview of digital audio and signal processing. It discusses representing sound digitally through sampling rate and bit rate. It describes problems like clipping, Nyquist frequency, and sample rate limits. It also covers topics like harmonics, fundamental frequency, dynamic range, and common signal processing tools. The document is presented by Elad Gariany and promotes their company Vidit, describing open roles there working on audio and video engineering.

Chapter 02 audio recording - part iNazihah Ahwan

The document provides an overview of analog and digital audio, including: - Analog audio uses continuous waves while digital audio represents sound as discrete numeric samples. - Key aspects of digital audio include sampling rate, bit depth, and channels. Higher sampling rates and bit depths provide more accurate representations. - Converting analog to digital audio involves sampling the amplitude over time using an analog-to-digital converter.

Sound Processing in MATLAB program speechleknasunitha

noise removal in matlabumarjamil10000

Fourier project presentation־�v ��

Vidyalankar final-essentials of communication systemsanilkurhekar

Audio Signal Processing Ahmed A. Arefin

Polyanna manualAleksi Eeben

The method of comparing two audio filesMinh Anh Nguyen

example based audio editingRamin Anushiravani

Digital Tunerplun

The method of comparing two audio filesMinh Anh Nguyen

Beamforming and microphone arraysRamin Anushiravani

Audio Data Representation (NCGD009)Adam Sporka

Tomas_IWAENC_keynote10.pptRakesh Pogula

DJ Workshop v.0.2bMagmakern Industries

What Shazam doesn't want you to knowRoy van Rijn

Session3Krutarth Patel

Audio125 wk1-review-digital-audio-specs-and-acousticsRaffiel203

Digital signal processing through speech, hearing, and PythonMel Chua

Digital Audio & Signal Processing (Elad Gariany)Ron Reiter

Chapter 02 audio recording - part iNazihah Ahwan

Recently uploaded (20)

Revolutionizing-Government-Communication-The-OSWAN-Success-Storyssuser52ad5e

? ????? ??????? ????? ? ???????? ??????????? is proud to be a part of the ?????? ????? ???? ???? ??????? (?????) success story! By delivering seamless, secure, and high-speed connectivity, OSWAN has revolutionized e-?????????? ?? ??????, enabling efficient communication between government departments and enhancing citizen services. Through our innovative solutions, ???????? ?????????? has contributed to making governance smarter, faster, and more transparent. This milestone reflects our commitment to driving digital transformation and empowering communities. ? ?????????? ??????, ?????????? ??????????!

DevNexus - Building 10x Development Organizations.pdfJustin Reock

Developer Experience is Dead! Long Live Developer Experience! In this keynote-style session, we��ll take a detailed, granular look at the barriers to productivity developers face today and modern approaches for removing them. 10x developers may be a myth, but 10x organizations are very real, as proven by the influential study performed in the 1980s, ��The Coding War Games.�� Right now, here in early 2025, we seem to be experiencing YAPP (Yet Another Productivity Philosophy), and that philosophy is converging on developer experience. It seems that with every new method, we invent to deliver products, whether physical or virtual, we reinvent productivity philosophies to go alongside them. But which of these approaches works? DORA? SPACE? DevEx? What should we invest in and create urgency behind today so we don��t have the same discussion again in a decade?

UiPath Document Understanding - Generative AI and Active learning capabilitiesDianaGray10

This session focus on Generative AI features and Active learning modern experience with Document understanding. Topics Covered: Overview of Document Understanding How Generative Annotation works? What is Generative Classification? How to use Generative Extraction activities? What is Generative Validation? How Active learning modern experience accelerate model training? Q/A ? If you have any questions or feedback, please refer to the "Women in Automation 2025" dedicated Forum thread. You can find there extra details and updates.

Field Device Management Market Report 2030 - TechSci ResearchVipin Mishra

The Global Field Device Management (FDM) Market is expected to experience significant growth in the forecast period from 2026 to 2030, driven by the integration of advanced technologies aimed at improving industrial operations. ? According to TechSci Research, the Global Field Device Management Market was valued at USD 1,506.34 million in 2023 and is anticipated to grow at a CAGR of 6.72% through 2030. FDM plays a vital role in the centralized oversight and optimization of industrial field devices, including sensors, actuators, and controllers. Key tasks managed under FDM include: Configuration Monitoring Diagnostics Maintenance Performance optimization FDM solutions offer a comprehensive platform for real-time data collection, analysis, and decision-making, enabling: Proactive maintenance Predictive analytics Remote monitoring By streamlining operations and ensuring compliance, FDM enhances operational efficiency, reduces downtime, and improves asset reliability, ultimately leading to greater performance in industrial processes. FDM��s emphasis on predictive maintenance is particularly important in ensuring the long-term sustainability and success of industrial operations. For more information, explore the full report: https://shorturl.at/EJnzR Major companies operating in Global?Field Device Management Market are: General Electric Co Siemens AG ABB Ltd Emerson Electric Co Aveva Group Ltd Schneider Electric SE STMicroelectronics Inc Techno Systems Inc Semiconductor Components Industries LLC International Business Machines Corporation (IBM) #FieldDeviceManagement #IndustrialAutomation #PredictiveMaintenance #TechInnovation #IndustrialEfficiency #RemoteMonitoring #TechAdvancements #MarketGrowth #OperationalExcellence #SensorsAndActuators

Q4 2024 Earnings and Investor PresentationDropbox

Brave Browser Crack 1.45.133 Activated 2025kherorpacca00126

https://ncracked.com/7961-2/ Note: >> Please copy the link and paste it into Google New Tab now Download link Brave is a free Chromium browser developed for Win Downloads, macOS and Linux systems that allows users to browse the internet in a safer, faster and more secure way than its competition. Designed with security in mind, Brave automatically blocks ads and trackers which also makes it faster, As Brave naturally blocks unwanted content from appearing in your browser, it prevents these trackers and pop-ups from slowing Download your user experience. It's also designed in a way that strips Downloaden which data is being loaded each time you use it. Without these components

Formal Methods: Whence and Whither? [Martin Fr?nzle Festkolloquium, 2025]Jonathan Bowen

Alan Turing arguably wrote the first paper on formal methods 75 years ago. Since then, there have been claims and counterclaims about formal methods. Tool development has been slow but aided by Moore��s Law with the increasing power of computers. Although formal methods are not widespread in practical usage at a heavyweight level, their influence as crept into software engineering practice to the extent that they are no longer necessarily called formal methods in their use. In addition, in areas where safety and security are important, with the increasing use of computers in such applications, formal methods are a viable way to improve the reliability of such software-based systems. Their use in hardware where a mistake can be very costly is also important. This talk explores the journey of formal methods to the present day and speculates on future directions.

Computational Photography: How Technology is Changing Way We Capture the WorldHusseinMalikMammadli

? Computational Photography (Computer Vision/Image): How Technology is Changing the Way We Capture the World He? d��?��nm��s��n��zm��, m��asir smartfonlar v? kameralar nec? bu q?d?r g?z?l g?r��nt��l?r yarad?r? Bunun sirri Computational Fotoqrafiyas?nda(Computer Vision/Imaging) gizlidir��??kill?ri ??km? v? emal etm? ��sulumuzu t?kmill??dir?n, komp��ter elmi il? fotoqrafiyan?n inqilabi birl??m?si.

Unlocking DevOps Secuirty :Vault & KeylockHusseinMalikMammadli

DevOps i? t?hl��k?sizliyi sizi maraqland?r?r? ?st?r developer, ist?r t?hl��k?sizlik m��h?ndisi, ist?rs? d? DevOps h?v?skar? olun, bu t?dbir ??b?k?l??m?k, bilikl?rinizi b?l��?m?k v? DevSecOps sah?sind? ?n son t?cr��b?l?ri ?yr?nm?k ��?��n m��k?mm?l f��rs?tdir! Bu workshopda DevOps infrastrukturlar?n?n t?hl��k?sizliyini nec? art?rmaq bar?d? dan??acay?q. DevOps sisteml?ri qurulark?n avtomatla?d?r?lm??, y��ks?k ?l?atan v? etibarl? olmas? il? yana??, h?m d? t?hl��k?sizlik m?s?l?l?ri n?z?r? al?nmal?d?r. Bu s?b?bd?n, DevOps komandolar?n?n t?hl��k?sizliy? y?n?lmi? praktikalara riay?t etm?si vacibdir.

Understanding Traditional AI with Custom Vision & MuleSoft.pptxshyamraj55

Understanding Traditional AI with Custom Vision & MuleSoft.pptx | ### �ݺ�ߣ Deck Description: This presentation features Atul, a Senior Solution Architect at NTT DATA, sharing his journey into traditional AI using Azure's Custom Vision tool. He discusses how AI mimics human thinking and reasoning, differentiates between predictive and generative AI, and demonstrates a real-world use case. The session covers the step-by-step process of creating and training an AI model for image classification and object detection��specifically, an ad display that adapts based on the viewer's gender. Atulavan highlights the ease of implementation without deep software or programming expertise. The presentation concludes with a Q&A session addressing technical and privacy concerns.

UiPath Automation Developer Associate Training Series 2025 - Session 2DianaGray10

In session 2, we will introduce you to Data manipulation in UiPath Studio. Topics covered: Data Manipulation What is Data Manipulation Strings Lists Dictionaries RegEx Builder Date and Time Required Self-Paced Learning for this session: Data Manipulation with Strings in UiPath Studio (v2022.10) 2 modules - 1h 30m - https://academy.uipath.com/courses/data-manipulation-with-strings-in-studio Data Manipulation with Lists and Dictionaries in UiPath Studio (v2022.10) 2 modules - 1h - https:/academy.uipath.com/courses/data-manipulation-with-lists-and-dictionaries-in-studio Data Manipulation with Data Tables in UiPath Studio (v2022.10) 2 modules - 1h 30m - https:/academy.uipath.com/courses/data-manipulation-with-data-tables-in-studio ?? For any questions you may have, please use the dedicated Forum thread. You can tag the hosts and mentors directly and they will reply as soon as possible.

Early Adopter's Guide to AI Moderation (Preview)nick896721

Build with AI on Google Cloud Session #4Margaret Maynard-Reid

30B Images and Counting: Scaling Canva's Content-Understanding Pipelines by K...ScyllaDB

THE BIG TEN BIOPHARMACEUTICAL MNCs: GLOBAL CAPABILITY CENTERS IN INDIASrivaanchi Nathan

This business intelligence report, "The Big Ten Biopharmaceutical MNCs: Global Capability Centers in India", provides an in-depth analysis of the operations and contributions of the Global Capability Centers (GCCs) of ten leading biopharmaceutical multinational corporations in India. The report covers AstraZeneca, Bayer, Bristol Myers Squibb, GlaxoSmithKline (GSK), Novartis, Sanofi, Roche, Pfizer, Novo Nordisk, and Eli Lilly. In this report each company's GCC is profiled with details on location, workforce size, investment, and the strategic roles these centers play in global business operations, research and development, and information technology and digital innovation.

DealBook of Ukraine: 2025 edition | AVentures CapitalYevgen Sysoyev

World Information Architecture Day 2025 - UX at a CrossroadsJoshua Randall

User Experience stands at a crossroads: will we live up to our potential to design a better world? or will we be co-opted by ��product management�� or another business buzzword? Looking backwards, this talk will show how UX has repeatedly failed to create a better world, drawing on industry data from Nielsen Norman Group, Baymard, MeasuringU, WebAIM, and others. Looking forwards, this talk will argue that UX must resist hype, say no more often and collaborate less often (you read that right), and become a true profession �� in order to be able to design a better world.

Future-Proof Your Career with AI OptionsDianaGray10

Learn about the difference between automation, AI and agentic and ways you can harness these to further your career. In this session you will learn: Introduction to automation, AI, agentic Trends in the marketplace Take advantage of UiPath training and certification In demand skills needed to strategically position yourself to stay ahead ? If you have any questions or feedback, please refer to the "Women in Automation 2025" dedicated Forum thread. You can find there extra details and updates.

Backstage Software Templates for Java DevelopersMarkus Eisele

Technology use over time and its impact on consumers and businesses.pptxkaylagaze

Revolutionizing-Government-Communication-The-OSWAN-Success-Storyssuser52ad5e

DevNexus - Building 10x Development Organizations.pdfJustin Reock

UiPath Document Understanding - Generative AI and Active learning capabilitiesDianaGray10

Field Device Management Market Report 2030 - TechSci ResearchVipin Mishra

Q4 2024 Earnings and Investor PresentationDropbox

Brave Browser Crack 1.45.133 Activated 2025kherorpacca00126

Formal Methods: Whence and Whither? [Martin Fr?nzle Festkolloquium, 2025]Jonathan Bowen

Computational Photography: How Technology is Changing Way We Capture the WorldHusseinMalikMammadli

Unlocking DevOps Secuirty :Vault & KeylockHusseinMalikMammadli

Understanding Traditional AI with Custom Vision & MuleSoft.pptxshyamraj55

UiPath Automation Developer Associate Training Series 2025 - Session 2DianaGray10

Early Adopter's Guide to AI Moderation (Preview)nick896721

Build with AI on Google Cloud Session #4Margaret Maynard-Reid

30B Images and Counting: Scaling Canva's Content-Understanding Pipelines by K...ScyllaDB

THE BIG TEN BIOPHARMACEUTICAL MNCs: GLOBAL CAPABILITY CENTERS IN INDIASrivaanchi Nathan

DealBook of Ukraine: 2025 edition | AVentures CapitalYevgen Sysoyev

World Information Architecture Day 2025 - UX at a CrossroadsJoshua Randall

Future-Proof Your Career with AI OptionsDianaGray10

Backstage Software Templates for Java DevelopersMarkus Eisele

Technology use over time and its impact on consumers and businesses.pptxkaylagaze

It's so quiet. Let's make music.

1. It��s so quiet. Let��s make music. Loren Segal RubyConf 2014

2. @lsegal

3. WARNING: MUSIC AHEAD ? ?

4. What is music even?

5. S + R = M ound hythm usic

6. S + R = M ound hythm usic

7. S + R = M ound uby usic

8. S + R = ound uby

9. S + R = ound uby This talk.

10. Sounddd beep. boop.

11. Analog Audio

12. Good Vibrations

13. Continuous Signal

14. DIGITAL AUDIO

15. ? ? ?

16. Discrete Signal (not continuous) 187 198 -84 -84

17. not continuous, Just Samples

18. Discrete Signal (not continuous)

19. Discrete Signal (not continuous)

20. Discrete Signal (sampling)

21. Discrete Signal (sampling)

22. Discrete Signal (sampling)

23. Audio Vocabulary [Sample Rate]? ��Number of data values for each second of audio�� Measured in Hertz (Hz)

24. 20 Samples 20.times.map {|i| (Math.sin(i) * 255).floor } [0, 214, 231, 35, -193, -245, -72, 167, 252, 105, -139, -255, -137, 107, 252, 165, -74, -246, -192, 38]

25. Common Sample Rates 44,100 Hz 44,800 Hz 96,000 Hz used by VHS, CDs

26. ? ?

27. fundamental WAVES

28. SINE sin(x)

29. #math

31. f(t) = A sin(2�Ц�t) s

32. f(t) = A sin(2�Ц�t) Amplitude Frequency s

33. Amplitude Frequency* ��ish

34. Audio Vocabulary [Amplitude]? ��Loudness�� Measured in Decibels (dB)

35. Audio Vocabulary [Frequency]? ��Pitch�� Measured in Hertz (Hz)

36. ��s s = Sample Rate = e.g., 44100

37. Frequency?

38. �� = 440 Hz [A3]

39. f(t) = sin(0.0627t)

41. SQUARE

42. f(t) = A ((��t) < 0.5 ? -1 : 1) s

43. Triangle

44. f(t) = A(1 - 4(��st - ��st ))

45. SAW(tooth)

46. f(t) = 2A (��t - ��t ) s s

47. NOISE

48. f(t) = A rand() rand() = some evenly distributed random fn

49. Show, Don��t Tell

50. Processing Audio

51. From synthesis

52. To analysis

53. Fourier Transform

54. It��s Complex

55. Literally.

56. SIDE VIEW OF f(t)!

57. thefouriertransform.com

58. ��All waveforms, no matter what you scribble or observe in the universe, are actually just the sum of simple sinusoids of different frequencies.��

64. Analysis Applications

65. 1. Pitch Detection: Instrument Tuner, Visualization

66. 2. Wave Shaping: Equalizer, Autotune

67. Let��s see it.

68. RUBY TIME

69. ruby-audio $ gem install

70. libsndfile wrapper

71. Write a Sine Wave require 'ruby-?�\audio' include RubyAudio sample_rate, len, freq, amp = 48000, 10, 440, 0.9 buf = Buffer.float(sample_rate * len) buf.size.times do |i| sine_rad = ((freq * Math::PI * 2) / sample_rate) * i buf[i] = amp * Math.sin(sine_rad) end format = FORMAT_WAV | FORMAT_PCM_16 info = SoundInfo.new(channels: 1, samplerate: sample_rate, format: format) snd = Sound.new('out.wav', 'w', info) snd.write(buf) snd.close

72. easy_audio $ gem install

73. portaudio wrapper

74. Play a Sine Wave require 'easy_audio' EasyAudio.easy_open(&EasyAudio::Waveforms::SINE) sleep 2 # play for 2 seconds

75. Increase Frequency / Time require 'easy_audio' triangle = EasyAudio::Waveforms::TRIANGLE stream = EasyAudio.easy_open(freq: 220, &triangle) Thread.new { loop { stream.frequency += 50; sleep 0.2 } } sleep 3

76. Audio Plugins

77. VST

78. github.com/lsegal/ easy_vst

79. Embedding DRb - C++ AudioEffect* createEffectInstance(audioMasterCallback audioMaster) { ruby_init(); ruby_init_loadpath(); rb_require("drb/drb"); rb_eval_string("DRb.start_service"); return new SampleVST(audioMaster); } // ... server = rb_eval_string( "DRbObject.new_with_uri('druby://localhost:9090')");

80. Call Distributed Method rb_funcall( server, // the DRb object rb_intern(��process��), // the remote method 1, // one argument INT2FIX(sampleFrames)); // number of samples

81. Fun Time!

82. Summary

83. 1. Audio is samples!

84. 2. Making waves! Sine, Square, Saw, Triangle, Noise

85. 3. Fourier = analysis

86. 4. Ruby libraries! ruby-audio easy_audio easy_vst

87. 5. Music!

88. ? gnuu.org

89. github.com/lsegal

90. @lsegal

91. Thanks! Questions?