jakobabesser / walking_bass_transcription_dnn

Algorithm for walking bass transcription in jazz ensemble recordings using Deep Neural Networks (DNN)

Walking Bass Transcription

Algorithm for walking bass transcription in jazz ensemble recordings using Deep Neural Networks (DNN)

• J. Abeßer, S. Balke, K. Frieler, M. Pfleiderer, M. Müller: Deep Learning for Jazz Walking Bass Transcription, AES conference on Semantic Audio, Erlangen, Germany, 2017

Audio examples can be found here:

• http://www.audiolabs-erlangen.de/resources/MIR/2017-AES-WalkingBassTranscription/

This algorithm was used to create the beat-wise bass pitch values included in the Weimar Jazz Database

• http://jazzomat.hfm-weimar.de/dbformat/dboverview.html

The script requires the Python packages

• python 3.X
• numpy
• scipy
• keras
• librosa
• pysoundfile
• h5py

We recommend you to install miniconda (https://conda.io/miniconda.html). You can create a suitable environment using

conda env create -f conda_environment.yml

and activated it via

source activate walking_bass_transcription

example

Now you can run the transcription algorithm on a test file by calling

python transcriber.py

After running the transcriber on the test file ArtPepper_Anthropology_Excerpt.wav, the bass saliency spectrogram is exported as CSV file ArtPepper_Anthropology_Excerpt_bass_pitch_saliency.csv, which can be imported into an empty pane in SonicVisualizer using the parameters

• Timing is specified: Implicitly: rows are equally spaced in time
• Audio sample rate (Hz): 22050
• Frame increment between rows: 1024

Here's an example of the output:

• WAV form
• STFT magnitude spectrogram
• bass saliency spectrogram

Enjoy.

adaptive tatum detection

Now the transcriber has a new aggregation method 'flex-q', which checks for each beat, which tatum subdivision is the most likely one. The note events are generated accordingly.

pitch saliency threshold

Furthermore, a lower saliency thresholds allows to ignore beats / sub-beats where the pitch saliency is too low.