Audio Impairment Recognition Using a Correlation-Based Feature Representation
This work addresses feature robustness issues in audio impairment recognition, which is important for audio processing applications, but it is incremental as it builds on existing hand-crafted features.
The paper tackles the problem of audio impairment recognition by proposing a correlation-based feature representation to improve feature robustness and computational efficiency, achieving comparable accuracy with reduced dimensionality and faster test-stage speed.
Audio impairment recognition is based on finding noise in audio files and categorising the impairment type. Recently, significant performance improvement has been obtained thanks to the usage of advanced deep learning models. However, feature robustness is still an unresolved issue and it is one of the main reasons why we need powerful deep learning architectures. In the presence of a variety of musical styles, hand-crafted features are less efficient in capturing audio degradation characteristics and they are prone to failure when recognising audio impairments and could mistakenly learn musical concepts rather than impairment types. In this paper, we propose a new representation of hand-crafted features that is based on the correlation of feature pairs. We experimentally compare the proposed correlation-based feature representation with a typical raw feature representation used in machine learning and we show superior performance in terms of compact feature dimensionality and improved computational speed in the test stage whilst achieving comparable accuracy.