# TACNET: Temporal Audio Source Counting Network

arXiv: https://arxiv.org/abs/2311.02369 · HTML: https://mlsanity.com/paper/2311.02369/

## 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 (most cited first, 22 of 22)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [PANNs: Large-Scale Pretrained Audio Neural Networks for Audio Pattern Recognition](https://mlsanity.com/paper/1912.10211/) (2019)
- [Conv-TasNet: Surpassing Ideal Time-Frequency Magnitude Masking for Speech Separation](https://mlsanity.com/paper/1809.07454/) (2018)
- [Deep clustering: Discriminative embeddings for segmentation and separation](https://mlsanity.com/paper/1508.04306/) (2015)
- [State-of-the-art Speech Recognition With Sequence-to-Sequence Models](https://mlsanity.com/paper/1712.01769/) (2017)
- [Attention is All You Need in Speech Separation](https://mlsanity.com/paper/2010.13154/) (2020)
- [Dual-path RNN: efficient long sequence modeling for time-domain single-channel speech separation](https://mlsanity.com/paper/1910.06379/) (2019)
- [Speaker Recognition from Raw Waveform with SincNet](https://mlsanity.com/paper/1808.00158/) (2018)
- [A Review of Speaker Diarization: Recent Advances with Deep Learning](https://mlsanity.com/paper/2101.09624/) (2021)
- [Deep Learning for Audio Signal Processing](https://mlsanity.com/paper/1905.00078/) (2019)
- [LEAF: A Learnable Frontend for Audio Classification](https://mlsanity.com/paper/2101.08596/) (2021)
- [Trainable Frontend For Robust and Far-Field Keyword Spotting](https://mlsanity.com/paper/1607.05666/) (2016)
- [ERANNs: Efficient Residual Audio Neural Networks for Audio Pattern Recognition](https://mlsanity.com/paper/2106.01621/) (2021)
- [Learning Filterbanks from Raw Speech for Phone Recognition](https://mlsanity.com/paper/1711.01161/) (2017)
- [Multi-talker ASR for an unknown number of sources: Joint training of source counting, separation and ASR](https://mlsanity.com/paper/2006.02786/) (2020)
- [Do We Need Sound for Sound Source Localization?](https://mlsanity.com/paper/2007.05722/) (2020)
- [Tackling real noisy reverberant meetings with all-neural source separation, counting, and diarization system](https://mlsanity.com/paper/2003.03987/) (2020)
- [CGCNN: Complex Gabor Convolutional Neural Network on raw speech](https://mlsanity.com/paper/2002.04569/) (2020)
- [Classification vs. Regression in Supervised Learning for Single Channel Speaker Count Estimation](https://mlsanity.com/paper/1712.04555/) (2017)
- [Multi-Decoder DPRNN: High Accuracy Source Counting and Separation](https://mlsanity.com/paper/2011.12022/) (2020)
- [Real-time Speaker counting in a cocktail party scenario using Attention-guided Convolutional Neural Network](https://mlsanity.com/paper/2111.00316/) (2021)
- [Improved Source Counting and Separation for Monaural Mixture](https://mlsanity.com/paper/2004.00175/) (2020)

## Built on by (most cited first, 2 of 2)

- [UNICBench: UNIfied Counting Benchmark for MLLM](https://mlsanity.com/paper/2603.00595/) (2026)
- [Training Large-Scale Optical Neural Networks with Two-Pass Forward Propagation](https://mlsanity.com/paper/2408.08337/) (2024)

## 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.