Exploration of Audio Quality Assessment and Anomaly Localisation Using Attention Models

TL;DR

This paper introduces a novel attention-based model combining bidirectional LSTM for audio quality assessment and anomaly localization, demonstrating improved performance on augmented speech datasets.

Contribution

The paper presents a new joint model for audio quality assessment and anomaly localization using attention mechanisms and LSTM, outperforming baseline methods.

Findings

Achieved about 5% improvement in correlation and F1 scores.

Effectively discriminates interference from desired signals.

Outperforms strong baseline methods on augmented TIMIT dataset.

Abstract

Many applications of speech technology require more and more audio data. Automatic assessment of the quality of the collected recordings is important to ensure they meet the requirements of the related applications. However, effective and high performing assessment remains a challenging task without a clean reference. In this paper, a novel model for audio quality assessment is proposed by jointly using bidirectional long short-term memory and an attention mechanism. The former is to mimic a human auditory perception ability to learn information from a recording, and the latter is to further discriminate interferences from desired signals by highlighting target related features. To evaluate our proposed approach, the TIMIT dataset is used and augmented by mixing with various natural sounds. In our experiments, two tasks are explored. The first task is to predict an utterance quality score, and the second is to identify where an anomalous distortion takes place in a recording. The obtained results show that the use of our proposed approach outperforms a strong baseline method and gains about 5% improvements after being measured by three metrics, Linear Correlation Coefficient and Spearman Rank Correlation Coefficient, and F1.