Analysis and design of gammatone signal models

Coherent Feature Extraction with Swarm Intelligence Based Hybrid Adaboost Weighted ELM Classification for Snoring Sound Classification

For patients suffering from obstructive sleep apnea and sleep-related breathing disorders, snoring is quite common, and it greatly interferes with the quality of life for them and for the people surrounding them. For diagnosing obstructive sleep apnea, snoring is used as a screening parameter, so the exact detection and classification of snoring sounds are quite important. Therefore, automated and very high precision snoring analysis and classification algorithms are required. In this work, initially the features are extracted from six different domains, such as time domain, frequency domain, Discrete Wavelet Transform (DWT) domain, sparse domain, eigen value domain, and cepstral domain. The extracted features are then selected using three efficient feature selection techniques, such as Golden Eagle Optimization (GEO), Salp Swarm Algorithm (SSA), and Refined SSA. The selected features are finally classified with the help of eight traditional machine learning classifiers and two proposed classifiers, such as the Firefly Algorithm-Weighted Extreme Learning Machine hybrid with Adaboost model (FA-WELM-Adaboost) and the Capuchin Search Algorithm-Weighted Extreme Learning Machine hybrid with Adaboost model (CSA-WELM-Adaboost). The analysis is performed on the MPSSC Interspeech dataset, and the best results are obtained when the DWT features with the refined SSA feature selection technique and FA-WELM-Adaboost hybrid classifier are utilized, reporting an Unweighted Average Recall (UAR) of 74.23%. The second-best results are obtained when DWT features are selected with the GEO feature selection technique and a CSA-WELM-Adaboost hybrid classifier is utilized, reporting an UAR of 73.86%.

The dynamic gammawarp auditory filterbank

Auditory filterbanks are an integral part of many metrics designed to predict speech intelligibility and speech quality. Considerations in these applications include accurate reproduction of auditory filter shapes, the ability to reproduce the impact of hearing loss as well as normal hearing, and computational efficiency. This paper presents an alternative method for implementing a dynamic compressive gammachirp (dcGC) auditory filterbank [Irino and Patterson (2006). IEEE Trans. Audio Speech Lang. Proc. 14, 2222-2232]. Instead of using a cascade of second-order sections, this approach uses digital frequency warping to give the gammawarp filterbank. The set of warped finite impulse response filter coefficients is constrained to be symmetrical, which results in the same phase response for all filters in the filterbank. The identical phase responses allow the dynamic variation in the gammachirp filter magnitude response to be realized as a sum, using time-varying weights, of three filters that provide the responses for high-, mid-, and low-intensity input signals, respectively. The gammawarp filterbank offers a substantial improvement in execution speed compared to previous dcGC implementations; for a dcGC filterbank, the gammawarp implementation is 24 to 38 times faster than the dcGC Matlab code of Irino.