Compressive speech enhancement using semi-soft thresholding and improved threshold estimation

Compressive speech enhancement is based on the compressive sensing (CS) sampling theory and utilizes the sparsity of the signal for its enhancement. To improve the performance of the discrete wavelet transform (DWT) basis-function based compressive speech enhancement algorithm, this study presents a semi-soft thresholding approach suggesting improved threshold estimation and threshold rescaling parameters. The semi-soft thresholding approach utilizes two thresholds, one threshold value is an improved universal threshold and the other is calculated based on the initial-silence-region of the signal. This study suggests that thresholding should be applied to both detail coefficients and approximation coefficients to remove noise effectively. The performances of the hard, soft, garrote and semi-soft thresholding approaches are compared based on objective quality and speech intelligibility measures. The normalized covariance measure is introduced as an effective intelligibility measure as it has a strong correlation with the intelligibility of the speech signal. A visual inspection of the output signal is used to verify the results. Experiments were conducted on the noisy speech corpus (NOIZEUS) speech database. The experimental results indicate that the proposed method of semi-soft thresholding using improved threshold estimation provides better enhancement compared to the other thresholding approaches

An audio processing pipeline for acquiring diagnostic quality heart sounds via mobile phone

Recently, heart sound signals captured using mobile phones have been employed to develop data-driven heart disease detection systems. Such signals are generally captured in person by trained clinicians who can determine if the recorded heart sounds are of diagnosable quality. However, mobile phones have the potential to support heart health diagnostics, even where access to trained medical professionals is limited. To adopt mobile phones as self-diagnostic tools for the masses, we would need to have a mechanism to automatically establish that heart sounds recorded by non-expert users in uncontrolled conditions have the required quality for diagnostic purposes. This paper proposes a quality assessment and enhancement pipeline for heart sounds captured using mobile phones. The pipeline analyzes a heart sound and determines if it has the required quality for diagnostic tasks. Also, in cases where the quality of the captured signal is below the required threshold, the pipeline can improve the quality by applying quality enhancement algorithms. Using this pipeline, we can also provide feedback to users regarding the cause of low-quality signal capture and guide them towards a successful one. We conducted a survey of a group of thirteen clinicians with auscultation skills and experience. The results of this survey were used to inform and validate the proposed quality assessment and enhancement pipeline. We observed a high level of agreement between the survey results and fundamental design decisions within the proposed pipeline. Also, the results indicate that the proposed pipeline can reduce our dependency on trained clinicians for capture of diagnosable heart sounds

A sign-preserving filter for signal decomposition

There are optimization problems in which an improvement in performance or a reduction in cost can be attained if the input signal of the system is split into multiple components. Splitting the signal allows customizing the design of the system’s hardware for a narrower range of frequencies, which in turn allows making a better use of its physical properties. There exist applications that have very specific signal-splitting requirements, such as ‘counter-flow avoidance’, that conventional signal processing tools cannot meet. Accordingly, a novel ‘Sign-Preserving’ filter has been developed and is presented in this article. The underlying algorithm of the filter is comprehensively explained with the aim of facilitating its reproduction, and the aspects of its operation are thoroughly discussed. The filter has two key features: (1) it separates a discrete signal a into two components – a mostly low-frequency signal b and a predominantly high-frequency signal c such that the sum of b and c replicates exactly the original signal a and, more importantly, (2) the signs of the two output signals are equal to the sign of a at all times. The article presents two case studies which demonstrate the use of the Sign-Preserving filter for the optimization of real-life applications, in which counter-flow must be avoided: the hybridization of the battery pack of an electric vehicle and the parallelization of a packed bed thermal energy store

Time Scale Modification (TSM) pada Sinyal Phonocardiogram (PCG) Menggunakan Synchronous Overlap-And-Add Fixed Synthesis (SOLAFS) dan Discrete Wavelet Transform (DWT)

Penelitian ini mengusulkan penerapan Time Scale Modification (TSM) berdasarkan dekomposisi multiresolusi dengan mengombinasikan Synchronous Overlap-and-Add Fixed Synthesis (SOLAFS) dan Discrete Wavelete Transform (DWT) pada sinyal Phonocardiogram (PCG) untuk memisahkan komponen bunyi jantung serta mereduksi noise. Sinyal PCG didekomposisi menjadi beberapa koefisien sub-band menggunakan DWT untuk mendapatkan informasi frekuensi yang dibutuhkan. SOLAFS diterapkan pada masing-masing koefisien sub-band, kemudian direkonstruksi menjadi sinyal hasil modifikasi skala waktu. Metode ini diuji pada 20 data rekaman PCG dengan rata-rata durasi 33 detik. Rekaman PCG terdiri dari sinyal normal dan abnormal yang masingmasing berjumlah 10 sinyal. Perbandingan visual pada domain waktu dan frekuensi digunakan untuk evaluasi kualitatif, sedangkan pengukuran kuantitatif dilakukan dengan menghitung nilai NRMSE dan SNR. =================================================================================================================================== The application of Time Scale Modification (TSM) based on multiresolution decomposition by combining Synchronous Overlap-andAdd Fixed Synthesis (SOLAFS) and Discrete Wavelete Transform (DWT) on Phonocardiogram (PCG) signals are proposed this study to separate heart sound components and reduce noise. PCG signals are decomposed into several sub-band coefficients using DWT to get the required frequency information. SOLAFS is applied to each sub-band coefficient, then reconstructed into time-scale modified signal. This method was tested on 20 PCG recordings with an average duration of 33 seconds. The recordings consist of normal and abnormal PCG with 10 signals each. Visual comparison of time and frequency domains is used for qualitative evaluation, while the quantitative measurement is done by calculating the NRMSE and SNR values