TACNET: Temporal Audio Source Counting Network
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Abstract (arXiv)
In this paper, we introduce the Temporal Audio Source Counting Network (TaCNet), an innovative architecture that addresses limitations in audio source counting tasks. TaCNet operates directly on raw audio inputs, eliminating complex preprocessing steps and simplifying the workflow. Notably, it excels in real-time speaker counting, even with truncated input windows. Our extensive evaluation, conducted using the LibriCount dataset, underscores TaCNet's exceptional performance, positioning it as a state-of-the-art solution for audio source counting tasks. With an average accuracy of 74.18 percentage over 11 classes, TaCNet demonstrates its effectiveness across diverse scenarios, including applications involving Chinese and Persian languages. This cross-lingual adaptability highlights its versatility and potential impact.
Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 22 of its 39 references that are in the map. First posted on arXiv 4 Nov 2023.
Builds on
22 of 39 references.
References · 22
- 2017Attention Is All You Needcited by 60,361
- 2019PANNs: Large-Scale Pretrained Audio Neural Networks for Audio Pattern Recognitioncited by 574
- 2018Conv-TasNet: Surpassing Ideal Time-Frequency Magnitude Masking for Speech Separationcited by 406
- 2015Deep clustering: Discriminative embeddings for segmentation and separationcited by 212
- 2017State-of-the-art Speech Recognition With Sequence-to-Sequence Modelscited by 196
- 2020Attention is All You Need in Speech Separationcited by 185
- 2019Dual-path RNN: efficient long sequence modeling for time-domain single-channel speech separationcited by 182
- 2018Speaker Recognition from Raw Waveform with SincNetcited by 166
- 2021A Review of Speaker Diarization: Recent Advances with Deep Learningcited by 124
- 2019Deep Learning for Audio Signal Processingcited by 92
- 2021LEAF: A Learnable Frontend for Audio Classificationcited by 69
- 2016Trainable Frontend For Robust and Far-Field Keyword Spottingcited by 33
10 more
- 2021ERANNs: Efficient Residual Audio Neural Networks for Audio Pattern Recognitioncited by 29
- 2017Learning Filterbanks from Raw Speech for Phone Recognitioncited by 25
- 2020Multi-talker ASR for an unknown number of sources: Joint training of source counting, separation and ASRcited by 15
- 2020Do We Need Sound for Sound Source Localization?cited by 15
- 2020Tackling real noisy reverberant meetings with all-neural source separation, counting, and diarization systemcited by 10
- 2020CGCNN: Complex Gabor Convolutional Neural Network on raw speechcited by 9
- 2017Classification vs. Regression in Supervised Learning for Single Channel Speaker Count Estimationcited by 5
- 2020Multi-Decoder DPRNN: High Accuracy Source Counting and Separationcited by 3
- 2021Real-time Speaker counting in a cocktail party scenario using Attention-guided Convolutional Neural Networkcited by 3
- 2020Improved Source Counting and Separation for Monaural Mixturecited by 1
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Citing papers per year: 2024: 1; 2025: 0; 2026: 1.
2 papers.