# Precision Scaling of Neural Networks for Efficient Audio Processing

arXiv: https://arxiv.org/abs/1712.01340 · HTML: https://mlsanity.com/paper/1712.01340/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Dec 2017.

## Built on by (most cited first, 3 of 3)

- [MoEVC: A Mixture-of-experts Voice Conversion System with Sparse Gating Mechanism for Accelerating Online Computation](https://mlsanity.com/paper/1912.11984/) (2019)
- [Faster Convolution Inference Through Using Pre-Calculated Lookup Tables](https://mlsanity.com/paper/2104.01681/) (2021)
- [SEOFP-NET: Compression and Acceleration of Deep Neural Networks for Speech Enhancement Using Sign-Exponent-Only Floating-Points](https://mlsanity.com/paper/2111.04436/) (2021)

## Abstract (arXiv)

While deep neural networks have shown powerful performance in many audio applications, their large computation and memory demand has been a challenge for real-time processing. In this paper, we study the impact of scaling the precision of neural networks on the performance of two common audio processing tasks, namely, voice-activity detection and single-channel speech enhancement. We determine the optimal pair of weight/neuron bit precision by exploring its impact on both the performance and processing time. Through experiments conducted with real user data, we demonstrate that deep neural networks that use lower bit precision significantly reduce the processing time (up to 30x). However, their performance impact is low (< 3.14%) only in the case of classification tasks such as those present in voice activity detection.