| Paper ID | SPE-30.2 |
| Paper Title |
IMPROVING AUDIO ANOMALIES RECOGNITION USING TEMPORAL CONVOLUTIONAL ATTENTION NETWORK |
| Authors |
Qiang Huang, Thomas Hain, University of Sheffield, United Kingdom |
| Session | SPE-30: Speech Processing 2: General Topics |
| Location | Gather.Town |
| Session Time: | Wednesday, 09 June, 16:30 - 17:15 |
| Presentation Time: | Wednesday, 09 June, 16:30 - 17:15 |
| Presentation |
Poster
|
| Topic |
Speech Processing: [SPE-SPER] Speech Perception and Psychoacoustics |
| IEEE Xplore Open Preview |
Click here to view in IEEE Xplore |
| Virtual Presentation |
Click here to watch in the Virtual Conference |
| Abstract |
Anomalous audio in speech recordings is often caused by speaker voice distortion, external noise, or even electric interferences. These obstacles have become a serious problem in some fields, such as high-quality music mixing and speech analysis. In this paper, a novel approach using a temporal convolutional attention network (TCAN) is proposed to tackle this problem. The use of temporal conventional network (TCN) can capture long range patterns using a hierarchy of temporal convolutional filters. To enhance the ability to tackle audio anomalies in different acoustic conditions, an attention mechanism is used in TCN, where a self-attention block is added after each temporal convolutional layer. This aims to highlight the target related features. To evaluate the performance of the proposed model, audio recordings are collected from the TIMIT dataset, and are then changed by adding five different types of audio distortions: gaussian noise, magnitude drift, random dropout, reduction of temporal resolution, and time warping. Distortions are mixed at different signal-to-noise ratios (SNRs) (5dB, 10dB, 15dB, 20dB, 25dB, 30dB). The experimental results show that the use of proposed model can yield better classification performances than some strong baseline methods, by approximate 3$\sim$ 10\% relative improvements. |
