Effects of noise suppression and envelope dynamic range compression on the intelligibility of vocoded sentences for a tonal language

Vocoder simulation studies have suggested that the carrier signal type employed affects the intelligibility of vocoded speech. The present work further assessed how carrier signal type interacts with additional signal processing, namely, single-channel noise suppression and envelope dynamic range compression, in determining the intelligibility of vocoder simulations. In Experiment 1, Mandarin sentences that had been corrupted by speech spectrum-shaped noise (SSN) or two-talker babble (2TB) were processed by one of four single-channel noise-suppression algorithms before undergoing tone-vocoded (TV) or noise-vocoded (NV) processing. In Experiment 2, dynamic ranges of multiband envelope waveforms were compressed by scaling of the mean-removed envelope waveforms with a compression factor before undergoing TV or NV processing. TV Mandarin sentences yielded higher intelligibility scores with normal-hearing (NH) listeners than did noise-vocoded sentences. The intelligibility advantage of noise-suppressed vocoded speech depended on the masker type (SSN vs 2TB). NV speech was more negatively influenced by envelope dynamic range compression than was TV speech. These findings suggest that an interactional effect exists between the carrier signal type employed in the vocoding process and envelope distortion caused by signal processing

Listening for Sirens: Locating and Classifying Acoustic Alarms in City Scenes

This paper is about alerting acoustic event detection and sound source localisation in an urban scenario. Specifically, we are interested in spotting the presence of horns, and sirens of emergency vehicles. In order to obtain a reliable system able to operate robustly despite the presence of traffic noise, which can be copious, unstructured and unpredictable, we propose to treat the spectrograms of incoming stereo signals as images, and apply semantic segmentation, based on a Unet architecture, to extract the target sound from the background noise. In a multi-task learning scheme, together with signal denoising, we perform acoustic event classification to identify the nature of the alerting sound. Lastly, we use the denoised signals to localise the acoustic source on the horizon plane, by regressing the direction of arrival of the sound through a CNN architecture. Our experimental evaluation shows an average classification rate of 94%, and a median absolute error on the localisation of 7.5{\deg} when operating on audio frames of 0.5s, and of 2.5{\deg} when operating on frames of 2.5s. The system offers excellent performance in particularly challenging scenarios, where the noise level is remarkably high.Comment: 6 pages, 9 figure