Concurrent Speakers Counter

Estimate the number of concurrent speakers from single channel mixtures to crack the "cocktail-party” problem which is based on a Bidirectional Long Short-Term Memory (BLSTM) which takes into account a past and future temporal context.

1. The model of the paper

"?" represents the number of samples.

2. My Model

3. Dependency Library

• librosa
• soundfile
• Keras (my test version: 2.1.1)
• Tensorflow (my test version: 1.4.0)
• Anaconda3 (Contains Python3.5+)

4. Dataset

It is called LibriCount10 0dB Dataset.

• contains a simulated cocktail party environment of [0..10] speakers
• mixed with 0dB SNR
• 5 seconds of recording
• 16bits, 16kHz, mono
• 11440 Samples, 832.5 MB

The annotation provides information about the speakers sex, their unique speaker_id, and vocal activity within the mixture recording in samples. The format of json file (3 speakers) is as follows:

[
    {
        "sex": "F",
        "activity": [[0, 51076], [51396, 55400], [56681, 80000]], 
        "speaker_id": 1221
    },
    {
        "sex": "F",
        "activity": [[0, 51877], [56201, 80000]],
        "speaker_id": 3570
    },
    {
        "sex": "M",
        "activity": [[0, 15681], [16161, 68213], [73498, 80000]], 
        "speaker_id": 5105
    }
]

5. Reference Paper

As we all know, it's pretty hard to solve the cocktail-party problem. This is the first study on data-driven speaker count estimation and the first step to crack the problem. Thanks for the author's paper[Paper 2] and code which help me a lot. Their homepage is AudioLabs Erlangen CountNet.

• Paper 1: Simon Leglaive, Romain Hennequin and Roland Badeau. Singing voice detection with deep recurrent neural networks (ICASSP 2015).
• Paper 2: Fabian-Robert Stöter, Soumitro Chakrabarty, Bernd Edler and Emanuël A. P. Habets. Classification vs. Regression in Supervised Learning for Single Channel Speaker Count Estimation (ICASSP2018).

6. Recommended links

• Coherence-Based Source Counter
• Speaker Count Estimation using Deep Neural Networks

6. Follow-up Work

I will work on speech separation for a long time. You can fork this repository if interested and pay close attention to my recent study.