Revisiting Singing Voice Detection : a quantitative review and the future outlook

This repo contains code for the paper "Revisiting Singing Voice Detection: a Quantitative Review and the Future Outlook" by Kyungyun Lee, Keunwoo Choi and Juhan Nam at the 19th International Society for Music Information Retrieval Conference (ISMIR) 2018. [pdf, blog post]

Requirements

• specified in requirements.txt

Public Dataset

• Jamendo with the same labeling, train/valid/test set split as described in the website.
• MedleyDB
  We used 61 songs that contain vocals, which can be found in medleydb_vocal_songs.txt.
  Note : MedleyDB does not provide vocal annotations, so we generated labels using the provided instrument activation annotation.
  Download the songs, change path, and run python medley_voice_label.py to generate labels for the 61 songs.

Dataset for stress testing (section 5)

To generate dataset, run

• python vibrato_data_gen.py for vibrato test in section 5.1.
• python snr_data_gen.py for SNR test in section 5.2. (Requires modification for path to MedleyDB vocal containing songs.)

Reproduction of singing voice detection models (section 3)

There are 3 reproduced models in the following folders :

• lehner_randomforest [1]
• schluter_cnn [2]
• leglaive_lstm [3]
  Note : Set paths for datasets in each config files within the model folders

Commandline arguments are :

• --model_name : whatever name you set it during training, and will be saved in ./weights/ folder.
• --dataset : one of {"jamendo", "vibrato", "snr"}. New dataset can be added with modification in load_data.py (might add RWC pop).

In each model folder, audio processor to preprocess data must be run before playing around with the model.

• python audio_processor.py --dataset "jamendo" in CNN and RNN model with {"jamendo", "vibrato", "snr"}
• python vocal_var.py --dataset "jamendo"" in randomforest model with {"jamendo", "vibrato", "snr"}
  Note : This file for randomforest computes vocal variance and concatenates them with the features extracted from the matlab code provided by the authors of [1]. So, this file only provides functions for computing the vocal variance. Either you can add onto this file to compute other features or you can find the matlab code ;)

To train models, run the following in each model folder

• python main.py --model_name "mynewmodel"

To run pretrained models (models are provided in ./weights/ folder), run the following in each model folder

• python test.py --model_name "mynewmodel" --dataset "jamendo"

References

• [1] Bernhard Lehner, Gerhard Widmer, and Reinhard Sonnleitner. "On the reduction of false positives in singing voice detection." pdf
• [2] Jan Schlueter and Thomas Grill. "Exploring data augmentation for improved singing voice detection with neural networks." pdf
• [3] Simon Leglaive, Romain Hennequin, and Roland Badeau. "Singing voice detection with deep recurrent neural network." pdf

TO DO (2018.06)

• Upload notebook file for model analysis and audacity compatible label generation.