vinceasvp/FCAC

Few-shot class-incremental audio classification, which can continually recognize novel audio classes without forgetting old ones

Few-shot Class-incremental Audio Classification Using Dynamically Expanded Classifier with Self-attention Modified Prototypes

This repository contains the introductions to the datasets and codes used in our paper, titled "Few-shot Class-incremental Audio Classification Using Dynamically Expanded Classifier with Self-attention Modified Prototypes " (as shown in the section of Citation).

Datasets

To study the Few-shot Class-incremental Audio Classification (FCAC) problem, three datasets of LS-100 dataset, NSynth-100 dataset and FSC-89 dataset are constructed by choosing samples from audio corpora of the Librispeech dataset, the NSynth dataset and the FSD-MIX-CLIPS dataset respectively.

Wei Xie, one of our team members, constructed the NSynth-100 dataset and FSC-89 dataset. The detailed information of these two datasets is here.

The detailed information of the LS-100 dataset is given below.

Statistics on the LS-100 dataset

	LS-100
Type of audio	Speech
Num. of classes	100 (60 of base classes, 40 of novel classes)
Num. of training / validation / testing samples per base class	500 / 150 / 100
Num. of training / validation / testing samples per novel class	500 / 150 / 100
Duration of the sample	All in 2 seconds
Sampling frequency	All in 16K Hz

Preparation of the LS-100 dataset

LibriSpeech is a corpus of approximately 1000 hours of 16kHz read English speech, prepared by Vassil Panayotov with the assistance of Daniel Povey. The data is derived from read audiobooks from the LibriVox project, and has been carefully segmented and aligned. We find that the subset train-clean-100 of Librispeech is enough for our study, so we constructed the LS-100 dataset using partial samples from the Librispeech as the source materials. To be specific, we first concatenate all the speakers' speech clips into a long speech, and then select the 100 speakers with the longest duration to cut their voices into two second speech. You can download the Librispeech from here.

1. Download dataset and extract the files.

2. Transfer the format of audio files. Move the script normalize-resample.sh to the root dirctory of extracted folder, and run the command bash normalize-resample.sh.

3. Construct LS-100 dataset.

   python data/LS100/construct_LS100.py --data_dir DATA_DIR --duration_json data/librispeech/spk_total_duration.json --single_spk_dir SINGLE_SPK_DIR --num_select_spk 100 --spk_segment_dir SPK_SEGMENT_DIR --csv_path CSV_PATH --spk_mapping_path SPK_MAPPING_PATH
   

Code

python train.py -project stdu -dataroot DATAROOT -dataset librispeech -config ./configs/stdu_LS-100_PQA_AP.yml -gpu 1

Acknowledgment

Our project references the codes in the following repos.

• CEC

Contact

Yanxiong Li (eeyxli@scut.edu.cn) and Wenchang Cao (wenchangcao98@163.com)

School of Electronic and Information Engineering, South China University of Technology, Guangzhou, China

Citation

Please cite our paper if you find the codes and datasets are useful for your research.

Y. Li, W. Cao, W. Xie, J. Li and E. Benetos, "Few-shot Class-incremental Audio Classification Using Dynamically Expanded Classifier with Self-attention Modified Prototypes," in IEEE Transactions on Multimedia, doi: 10.1109/TMM.2023.3280011. https://ieeexplore.ieee.org/document/10135116.