Development of algorithms for smart hearing protection devices

In industrial environments, wearing hearing protection devices is required to protect the wearers from high noise levels and prevent hearing loss. In addition to their protection against excessive noise, hearing protectors block other types of signals, even if they are useful and convenient. Therefore, if people want to communicate and exchange information, they must remove their hearing protectors, which is not convenient, or even dangerous. To overcome the problems encountered with the traditional passive hearing protection devices, this thesis outlines the steps and the process followed for the development of signal processing algorithms for a hearing protector that allows protection against external noise and oral communication between wearers. This hearing protector is called the “smart hearing protection device”. The smart hearing protection device is a traditional hearing protector in which a miniature digital signal processor is embedded in order to process the incoming signals, in addition to a miniature microphone to pickup external signals and a miniature internal loudspeaker to transmit the processed signals to the protected ear. To enable oral communication without removing the smart hearing protectors, signal processing algorithms must be developed. Therefore, the objective of this thesis consists of developing a noise-robust voice activity detection algorithm and a noise reduction algorithm to improve the quality and intelligibility of the speech signal. The methodology followed for the development of the algorithms is divided into three steps: first, the speech detection and noise reduction algorithms must be developed, second, these algorithms need to be evaluated and validated in software, and third, they must be implemented in the digital signal processor to validate their feasibility for the intended application. During the development of the two algorithms, the following constraints must be taken into account: the hardware resources of the digital signal processor embedded in the hearing protector (memory, number of operations per second), and the real-time constraint since the algorithm processing time should not exceed a certain threshold not to generate a perceptible delay between the active and passive paths of the hearing protector or a delay between the lips movement and the speech perception. From a scientific perspective, the thesis determines the thresholds that the digital signal processor should not exceed to not generate a perceptible delay between the active and passive paths of the hearing protector. These thresholds were obtained from a subjective study, where it was found that this delay depends on different parameters: (a) the degree of attenuation of the hearing protector, (b) the duration of the signal, (c) the level of the background noise, and (d) the type of the background noise. This study showed that when the fit of the hearing protector is shallow, 20 % of participants begin to perceive a delay after 8 ms for a bell sound (transient), 16 ms for a clean speech signal and 22 ms for a speech signal corrupted by babble noise. On the other hand, when having a deep hearing rotection fit, it was found that the delay between the two paths is 18 ms for the bell signal, 26 ms for the speech signal without noise and no delay when speech is corrupted by babble noise, showing that a better attenuation allows more time for digital signal processing. Second, this work presents a new voice activity detection algorithm in which a low complexity speech characteristic has been extracted. This characteristic was calculated as the ratio between the signal’s energy in the frequency region that contains the first formant to characterize the speech signal, and the low or high frequencies to characterize the noise signals. The evaluation of this algorithm and its comparison to another benchmark algorithm has demonstrated its selectivity with a false positive rate averaged over three signal to noise ratios (SNR) (10, 5 and 0 dB) of 4.2 % and a true positive rate of 91.4 % compared to 29.9 % false positives and 79.0 % of true positives for the benchmark algorithm. Third, this work shows that the extraction of the temporal envelope of a signal to generate a nonlinear and adaptive gain function enables the reduction of the background noise, the improvement of the quality of the speech signal and the generation of the least musical noise compared to three other benchmark algorithms. The development of speech detection and noise reduction algorithms, their objective and subjective evaluations in different noise environments, and their implementations in digital signal processors enabled the validation of their efficiency and low complexity for the the smart hearing protection application