Audio processing in Python with Feature Extraction for machine learning

Python library librosa is a python package for music and audio analysis. It provides the building blocks necessary to create music information retrieval systems. librosa uses soundfile and audioread to load audio files. Note that soundfile does not currently support MP3, which will cause librosa to fall back on the audioread library. Library Highlights: CoreIO and DSP Feature Extraction Onset Detection Beat and Tempo Spectrogram decomposition Temporal segmentation Sequential modeling Viterbi decoding¶ Content Timeline: ----------------- (00:00) Video Start (00:08) Content Introduction (02:27) Python Audio processing resources (04:08) Tutorial Source code intro (04:40) Tutorial Starts (05:18) Royalty free audio (05:42) Audio processing with librosa (16:25) Beats retrieval from audio (18:35) Beats Generation (21:01) Features Extraction (21:25) Zero Crossing Rate (24:37) Spectral Centroid (27:30) Spectral Rolloff (28:58) MFCCs (33:24) Chroma Frequencies (36:46) RMS Root-mean-square (41:26) Code Push to GitHub (42:10) Recap (43:29) Credits Python librosa url: https://github.com/librosa/librosa Source Code used in this example: https://github.com/prodramp/publiccod... Please visit: ------------------ Prodramp LLC | https://prodramp.com | @prodramp   / prodramp   Content Creator: Avkash Chauhan (@avkashchauhan)   / avkashchauhan   Tags: #ai #aicloud #h2oai #driverlessai #machinelearning #cloud #mlops #model #collaboration #deeplearning #modelserving #modeldeployment #keras #tensorflow #pytorch #datarobot #datahub #aiplatform #aicloud #modelperformance #modelfit #modeleffect #modelimpact #bias #modelbias #modeldeployment #modelregistery #modelpipeline #neptuneai #librosa #pythondsp #pythonaudio