Low-Complexity Signal Processing for Speech Enhancement and Audio Analysis

In real-time signal processing there is a constraint to finish processing of an audio signal before the next audio segment is received. This makes it important to have signal processing algorithms with low computational complexity while still maintaining high quality results. This thesis presents methods for audio signal processing used in real-time systems. The publications presented cover areas of noise reduction, network echo cancellation, noise dosimeter measurements and voice analysis. A method for speech enhancement is presented with low amounts of speech distortion. The audio signal is split into several subbands, covering different frequency regions. For each subband, the noise level is estimated. A signal gain is calculated by comparing the total signal level with the noise level for each subband. The method presented here, improves performance compared to previously similar methods. Improvement is especially found in multi-speaker and noise-only scenarios. When communicating on a telephone line, network echo is introduced by hybrids in the network. In cases where multiple echo sources exist, the time range for echoes can be quite long. In devices with limited storage, it is difficult to get good echo cancellation in such cases. This thesis presents a method for network echo cancellation suited for use in a device with a larger external memory. Exposure to high noise levels will have negative health effects and methods for measuring noise level exposure is important. Included in this thesis is a study that remove the influence of own voice in noise dose measurements. For certain medical conditions it is beneficial with daily voice exercises. Methods for grading voice in four different exercises is presented, based on pitch and loudness. Evaluation is done in real-time on test medical device