# Zero-Shot Audio Classification with Factored Linear and Nonlinear Acoustic-Semantic Projections

arXiv: https://arxiv.org/abs/2011.12657 · HTML: https://mlsanity.com/paper/2011.12657/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Nov 2020.

## Built on by (most cited first, 6 of 6)

- [Audio-visual Generalised Zero-shot Learning with Cross-modal Attention and Language](https://mlsanity.com/paper/2203.03598/) (2022)
- [I can listen but cannot read: An evaluation of two-tower multimodal systems for instrument recognition](https://mlsanity.com/paper/2407.18058/) (2024)
- [Zero-Shot Audio Classification using Image Embeddings](https://mlsanity.com/paper/2206.04984/) (2022)
- [Improved Zero-Shot Audio Tagging & Classification with Patchout Spectrogram Transformers](https://mlsanity.com/paper/2208.11402/) (2022)
- [Multi-label Zero-Shot Audio Classification with Temporal Attention](https://mlsanity.com/paper/2409.00408/) (2024)
- [A Benchmark of Generative Methods for Zero-Shot Environmental Sound Classification](https://mlsanity.com/paper/2412.03771/) (2024)

## Abstract (arXiv)

In this paper, we study zero-shot learning in audio classification through factored linear and nonlinear acoustic-semantic projections between audio instances and sound classes. Zero-shot learning in audio classification refers to classification problems that aim at recognizing audio instances of sound classes, which have no available training data but only semantic side information. In this paper, we address zero-shot learning by employing factored linear and nonlinear acoustic-semantic projections. We develop factored linear projections by applying rank decomposition to a bilinear model, and use nonlinear activation functions, such as tanh, to model the non-linearity between acoustic embeddings and semantic embeddings. Compared with the prior bilinear model, experimental results show that the proposed projection methods are effective for improving classification performance of zero-shot learning in audio classification.