  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 processing. 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 and
mitigate the interferences from irrelevant information. 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, such as the LSTM
and TCN based models, by approximate 3$\sim$ 10\% relative improvements.
