aiamirul / dcase16-cnn

Sound event detection in real life audio with CNN submitted to DCASE16

CNN based DCASE 2016 sound event detection system

Sound event detection system submitted to DCASE 2016 (detection and classification of acoustic scenes and events) challenge.

Convolutional neural network is used for detecting and classifying polyphonic events in a long temporal context of filter bank acoustic features. Training data are augmented vi sox speed perturbation.

On development data set the system achieves 0.84% segment error rate (7.7% relative imporment compared to baseline) 36.3% F-measure (55.1 relative better than baseline system).

Technical details are descibed in the challenge report. Detailed results summary on development and evaluation audios are also available:

Basic usage

run-cnn-pipeline.sh - complete self-documented script for reproducing all the experiments including the following:

• task3_gmm_baseline.py - baseline GMM system provided by organizers.

• src/make_downsample.sh - basic data preparation (down sampling)

• task3_cnn.py - run CNN based system training and testing

• src/make_speed.sh - speed perturbation