jonnor / Machinehearing
Machine Learning applied to sound
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Machine Hearing
Classifying sound using Machine Learning
At KnowIt Oslo, 2020. Video recording, slides, notes
Environmental Sound Classification on Microcontrollers using Convolutional Neural Networks
Master thesis. Report and code available in the Github repository.
EuroPython2019: Audio Classification using Machine Learning
Presentation at EuroPython2019. Video recording, notes
PyCode2019: Recognizing sounds with Machine Learning and Python
Presentation at PyCode Conference 2019 in Gdansk. Slides, notes
Video recording. Coming, maybe in November.
SenseCamp2019: Classification of Environmental Sound using IoT sensors
Presentation at SenseCamp 2019 hosted by FORCE Technology Senselab. Slides: web, .PDF
NMBU lecture on Audio Classification
Report and lecture at NMBU Data Science.
Stack Overflow answers
With example code in Python
- Loading Youtube audio data with youtube-dl and librosa
- Extracting fixed-size analysis windows from audio
- Classifying an audio clip of many analysis windows using Keras Timedistributed and GlobalAveragePooling
- Classifying an audio clip by voting over analysis windows. Mean/majority voting.
- Annotating/labeling audio data using Audacity
- Preprocessing audio into mel-spectrograms
- Multi-core preprocessing of audio files using joblib
- Compute MFCC or mel-spectrogram from existing STFT spectrograms
- Converting mel-spectrograms into PNG images
- Converting mel-spectrogram or MFCC back to audio waveform using librosa
Notes
Rough notes on various topics.
- Applications. Practical applications of Machine Hearing
- Tasks. Established problem formulations
- Audio Quality. Metrics for measuring audio quality
- Explainable models for Audio.
- Features. Feature representations
- Preprocessing. Preprocessing techniques
- DCASE2018. Notes from DCASE2018 challenge and conference
- Commercial solutions. Companies and products in Machine Hearing
- Speech. Speech-specific techniques and applications
- Music. Music-specific techniques and applications
- Compressive Sensing.
Resources
Useful resources to learn more.
Presentations
- Audio Event Detection w/Deep Learning. By Robert Coop, Ph.D, Head of AI and ML @ Stanley B&D. From Data Science Connect, 2028.
Books
- Computational Analysis of Sound Scenes and Events. Tuomas Virtanen, Mark D. Plumbley, Dan Ellis. 2018.
- Human and Machine Hearing - Extracting Meaning from Sound. Richard F. Lyon. 2017, revised 2018.
- An Introduction to Audio Content Analysis - Applications in Signal Processing and Music Informatics. Alexander Lerch. 2012. Companion website: https://www.audiocontentanalysis.org/
- Machine Learning for Audio, Image and Video Analysis: Theory and Applications (Advanced Information and Knowledge Processing). Francesco Camastra, 3 sections. From Perception to Computation, Machine Learning, Applications.
Online courses
- CSC 83060: Speech and Audio Understanding. http://mr-pc.org/t/csc83060/ Brooklyn College (CUNY).
Software
Feature extraction
- librosa. The go-to Python module.
- essentia. C++ library, with Python bindings. Lots of Music Analysis extractors. Used by FreeSound and Acousticbrainz.
- kapre. On-demand GPU computation of melspectrograms, for Keras
- torchaudio.
Data Augmentation
Lecture notes
- Audio Classification. http://www.cs.tut.fi/~sgn24006/PDF/L04-audio-classification.pdf Covers low-level features, MFCC. Classification by distance metrics. GMM. HMM.
- Speech Signal Analysis, Lecture 2. January 2017, Hiroshi Shimodaira and Steve Renals. ! great diagrams of audio discretization, mel filters, wide versus narrow-band spectrograms.
Competions
- Kaggle Whale detection
- Kaggle FreeSound tagging 2018
- Kaggle FreeSound
- DCASE2014
- DCASE2018
- DCASE2019
Datasets
Online Communities
- https://mircommunity.slack.com/ - Music Information Retrieval
Lists
- Awesome Deep Learning Music
- Fast.ai forums: Deep Learning with Audio. Large lists of resources, both in first post and "popular links". Feb 2019, 315 replies over 4 months.
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