At KnowIt Oslo, 2020. Video recording, slides, notes
Master thesis. Report and code available in the Github repository.
Presentation at EuroPython2019. Video recording, notes
Presentation at PyCode Conference 2019 in Gdansk. Slides, notes
Video recording. Coming, maybe in November.
Presentation at SenseCamp 2019 hosted by FORCE Technology Senselab. Slides: web, .PDF
Report and lecture at NMBU Data Science.
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
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.
Useful resources to learn more.
- Audio Event Detection w/Deep Learning. By Robert Coop, Ph.D, Head of AI and ML @ Stanley B&D. From Data Science Connect, 2028.
- 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.
- CSC 83060: Speech and Audio Understanding. http://mr-pc.org/t/csc83060/ Brooklyn College (CUNY).
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
- 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.
- Kaggle Whale detection
- Kaggle FreeSound tagging 2018
- Kaggle FreeSound
- DCASE2014
- DCASE2018
- DCASE2019
- https://mircommunity.slack.com/ - Music Information Retrieval
- 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.