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Acquiring clear acoustic signals is critical for the analysis of animal vocalizations. Bioacoustics studies commonly face the problem of overlapping signals, which can impede the structural identification of vocal units, but there is currently no satisfactory solution. This study presents a bi‐directional long short‐term memory network to separate overlapping echolocation‐communication calls of 6 different bat species and reconstruct waveforms. The separation quality was evaluated using 7 temporal‐spectrum parameters. All the echolocation pulses and syllables of communication calls in the overlapping signals were separated and parameter comparisons showed no significant difference and negligible deviation between the extracted and original calls. Clustering analysis was conducted with separated echolocation calls from each bat species to provide an example of practical application of the separated and reconstructed calls. The result of clustering analysis showed high corrected rand index (82.79%), suggesting the reconstructed waveforms could be reliably used for species classification. These results demonstrate a convenient and automated approach for separating overlapping calls. The study extends the application of deep neural networks to separate overlapping animal sounds. [ABSTRACT FROM AUTHOR]

Abnormal noise originating from within faulty products often irritates customers, which may lead to expensive warranty claims. Therefore, it is important to identify these faulty products proactively in the manufacturing process. However, noise detection in a loud shop-floor is not straightforward because inspection in an anechoic chamber is very costly, and some prerequisites for conventional noise reduction and source separation methods, such as stationary and independent signals and prior knowledge about the signal of interest, are sometimes not feasible in practice. Therefore, we developed an in-process noise inspection system that supports dual-channel acoustic data collection during the inspection process. By using two different groups of acoustic signals, abnormal sound separation and noise detection are made possible through three main steps: in-process background noise training, abnormal noise separation, and significance evaluation. The efficiency of the proposed procedure is demonstrated with two case studies: car door trim panels and dual-channel sound generator and collector. [ABSTRACT FROM AUTHOR]

Objective: Listening to music is still one of the more challenging aspects of using a cochlear implant (CI) for most users. Simple musical structures, a clear rhythm/beat, and lyrics that are easy to follow are among the top factors contributing to music appreciation for CI users. Modifying the audio mix of complex music potentially improves music enjoyment in CI users. Methods: A stereo music preprocessing scheme is described in which vocals, drums, and bass are emphasized based on the representation of the harmonic and the percussive components in the input spectrogram, combined with the spatial allocation of instruments in typical stereo recordings. The scheme is assessed with postlingually deafened CI subjects (N = 7) using pop/rock music excerpts with different complexity levels. Results: The scheme is capable of modifying relative instrument level settings, with the aim of improving music appreciation in CI users, and allows individual preference adjustments. The assessment with CI subjects confirms the preference for more emphasis on vocals, drums, and bass as offered by the preprocessing scheme, especially for songs with higher complexity. Conclusion: The stereo music preprocessing scheme has the potential to improve music enjoyment in CI users by modifying the audio mix in widespread (stereo) music recordings. Significance: Since music enjoyment in CI users is generally poor, this scheme can assist the music listening experience of CI users as a training or rehabilitation tool. [ABSTRACT FROM PUBLISHER]

Separating singing voice from music accompaniment is very useful in many applications, such as lyrics recognition and alignment, singer identification, and music information retrieval. Although speech separation has been extensively studied for decades, singing voice separation has been little investigated. We propose a system to separate singing voice from music accompaniment for monaural recordings. Our system consists of three stages. The singing voice detection stage partitions and classifies an input into vocal and nonvocal portions. For vocal portions, the predominant pitch detection stage detects the pitch of the singing voice and then the separation stage uses the detected pitch to group the time-frequency segments of the singing voice. Quantitative results show that the system performs the separation task successfully. [ABSTRACT FROM AUTHOR]

Wheezing reveals important cues that can be useful in alerting about respiratory disorders, such as Chronic Obstructive Pulmonary Disease. Early detection of wheezing through auscultation will allow the physician to be aware of the existence of the respiratory disorder in its early stage, thus minimizing the damage the disorder can cause to the subject, especially in low-income and middle-income countries. The proposed method presents an extended version of Non-negative Matrix Partial Co-Factorization (NMPCF) that eliminates most of the acoustic interference caused by normal respiratory sounds while preserving the wheezing content needed by the physician to make a reliable diagnosis of the subject's airway status. This extension, called Informed Inter-Segment NMPCF (IIS-NMPCF), attempts to overcome the drawback of the conventional NMPCF that treats all segments of the spectrogram equally, adding greater importance for signal reconstruction of repetitive sound events to those segments where wheezing sounds have not been detected. Specifically, IIS-NMPCF is based on a bases sharing process in which inter-segment information, informed by a wheezing detection system, is incorporated into the factorization to reconstruct a more accurate modelling of normal respiratory sounds. Results demonstrate the significant improvement obtained in the wheezing sound quality by IIS-NMPCF compared to the conventional NMPCF for all the Signal-to-Noise Ratio (SNR) scenarios evaluated, specifically, an SDR, SIR and SAR improvement equals 5.8 dB, 4.9 dB and 7.5 dB evaluating a noisy scenario with SNR = −5 dB. [ABSTRACT FROM AUTHOR]