Audio Enhancement for Computer Audition -- An Iterative Training Paradigm Using Sample Importance
This work addresses noise robustness for computer audition applications in everyday-life noisy environments, representing an incremental improvement over existing methods.
The paper tackles the problem of noise contamination in audio tasks like speech recognition and acoustic scene classification by proposing an end-to-end training paradigm that jointly optimizes audio enhancement and target applications using sample importance. The result is a significant boost in noise robustness, especially at low signal-to-noise ratios, across various tasks.
Neural network models for audio tasks, such as automatic speech recognition (ASR) and acoustic scene classification (ASC), are susceptible to noise contamination for real-life applications. To improve audio quality, an enhancement module, which can be developed independently, is explicitly used at the front-end of the target audio applications. In this paper, we present an end-to-end learning solution to jointly optimise the models for audio enhancement (AE) and the subsequent applications. To guide the optimisation of the AE module towards a target application, and especially to overcome difficult samples, we make use of the sample-wise performance measure as an indication of sample importance. In experiments, we consider four representative applications to evaluate our training paradigm, i.e., ASR, speech command recognition (SCR), speech emotion recognition (SER), and ASC. These applications are associated with speech and non-speech tasks concerning semantic and non-semantic features, transient and global information, and the experimental results indicate that our proposed approach can considerably boost the noise robustness of the models, especially at low signal-to-noise ratios (SNRs), for a wide range of computer audition tasks in everyday-life noisy environments.