The computing performance optimization of the Short-Lag Spatial Coherence (SLSC) method applied to ultrasound data processing is presented. The method is based on the theory that signals from adjacent receivers are correlated, drawing on a simplified conclusion of the van Cittert-Zernike theorem. It has been proven that it can be successfully used in ultrasound data reconstruction with despeckling. Former works have shown that the SLSC method in its original form has two main drawbacks: time-consuming processing and low contrast in the area near the transceivers. In this study, we introduce a method that allows to overcome both of these drawbacks.
The presented approach removes the dependency on distance (the “lag” parameter value) between signals used to calculate correlations. The approach has been tested by comparing results obtained with the original SLSC algorithm on data acquired from tissue phantoms.
The modified method proposed here leads to constant complexity, thus execution time is independent of the lag parameter value, instead of the linear complexity. The presented approach increases computation speed over 10 times in comparison to the base SLSC algorithm for a typical lag parameter value. The approach also improves the output image quality in shallow areas and does not decrease quality in deeper areas.
The Open Seminar on Acoustics is an annual conference, the largest acoustics conference in the country. It has been bringing all Polish acousticians together for over sixty years. It is organized in turns by different divisions of Polish Acoustical Society – in 2019 by the Poznan Division with the Institute of Acoustics, Adam Mickiewicz University in Poznan and Committee on Acoustics of Polish Academy of Science. The conference presents all sections of acoustics, such as: physical acoustics, technical, environmental, speech, hearing, musical, architectural acoustics, etc. The seminar is joined with special session “New trends in psychoacoustics in tribute to professors: Józef Zwisłocki and Andrzej Rakowski” and the Workshop “Noise protection in regulations – current state and directions of changes” (in Polish). We also invite you to the special session “Advances in research in the field of audio acoustics and sound engineering – ISSET 2019”.
The human voice is one of the basic means of communication, thanks to which one also can easily convey the emotional state. This paper presents experiments on emotion recognition in human speech based on the fundamental frequency. AGH Emotional Speech Corpus was used. This database consists of audio samples of seven emotions acted by 12 different speakers (6 female and 6 male). We explored phrases of all the emotions – all together and in various combinations. Fast Fourier Transformation and magnitude spectrum analysis were applied to extract the fundamental tone out of the speech audio samples. After extraction of several statistical features of the fundamental frequency, we studied if they carry information on the emotional state of the speaker applying different AI methods. Analysis of the outcome data was conducted with classifiers: K-Nearest Neighbours with local induction, Random Forest, Bagging, JRip, and Random Subspace Method from algorithms collection for data mining WEKA. The results prove that the fundamental frequency is a prospective choice for further experiments.
The most challenging in speech enhancement technique is tracking non-stationary noises for long speech segments and low Signal-to-Noise Ratio (SNR). Different speech enhancement techniques have been proposed but, those techniques were inaccurate in tracking highly non-stationary noises. As a result, Empirical Mode Decomposition and Hurst-based (EMDH) approach is proposed to enhance the signals corrupted by non-stationary acoustic noises. Hurst exponent statistics was adopted for identifying and selecting the set of Intrinsic Mode Functions (IMF) that are most affected by the noise components. Moreover, the speech signal was reconstructed by considering the least corrupted IMF. Though it increases SNR, the time and resource consumption were high. Also, it requires a significant improvement under nonstationary noise scenario. Hence, in this article, EMDH approach is enhanced by using Sliding Window (SW) technique. In this SWEMDH approach, the computation of EMD is performed based on the small and sliding window along with the time axis. The sliding window depends on the signal frequency band. The possible discontinuities in IMF between windows are prevented by the total number of modes and the number of sifting iterations that should be set a priori. For each module, the number of sifting iterations is determined by decomposition of many signal windows by standard algorithm and calculating the average number of sifting steps for each module. Based on this approach, the time complexity is reduced significantly with suitable quality of decomposition. Finally, the experimental results show the considerable improvements in speech enhancement under non-stationary noise environments.
The aim of the study was to examine the relationship between tinnitus pitch and maximum hearing loss, frequency range of hearing loss, and the edge frequency of the audiogram, as well as, to analyze tinnitus loudness at tinnitus frequency and normal hearing frequency.
The study included 212 patients, aged between 21 to 75 years (mean age of 54.4 ± 13.5 years) with chronic subjective tinnitus and sensorineural hearing loss. For the statistical data analysis we used Chisquare test and Fisher’s exact test with level of significance p < 0:05.
Tinnitus pitch corresponding to the frequency range of hearing loss, maximum hearing loss and the edge frequency was found in 70.8%, 37.3%, and 16.5% of the patients, respectively. The majority of patients had tinnitus pitch from 3000 to 8000 Hz corresponding to the range of hearing loss (p < 0:001). The mean tinnitus pitch was 3545 Hz ± 2482. The majority (66%) of patients had tinnitus loudness 4–7 dB SL. The mean sensation level at tinnitus frequency was 4.9 dB SL ± 1.9, and 13 dB SL ± 2.9 at normal hearing frequency.
Tinnitus pitch corresponded to the frequency range of hearing loss in majority of patients. There was no relationship between tinnitus pitch and the edge frequency of the audiogram. Loudness matching outside the tinnitus frequency showed higher sensation level than loudness matching at tinnitus frequency.
The effects of friction were observed in electric guitar strings passing over an electric guitar saddle. The effects of changing the ratio of the diameter of the winding to the diameter of the core of the string, the angle through which the string is bent, and the length on either side of the saddle were measured. Relative tensions were deduced by plucking and measuring the frequencies of vibration of the two portions of string. Coefficients of friction consistent with the capstan equation were calculated and were found to be lower than 0.26 for wound strings (nickel plated steel windings on steel cores) and lower than 0.17 for unwound (tin plated steel) strings. The largest values of friction were associated with strings of narrower windings and wider cores and this may be due to the uneven nature of the contact between the string and saddle for wound strings or due the surface of the windings deforming more, encouraging fresh (and therefore higher friction) metal to metal contact. It is advised to apply lubrication under the saddle to string contact point after first bringing the string up to pitch rather than before in order to prevent this fresh metal to metal contact.
The paper presents the key-finding algorithm based on the music signature concept. The proposed music signature is a set of 2-D vectors which can be treated as a compressed form of representation of a musical content in the 2-D space. Each vector represents different pitch class. Its direction is determined by the position of the corresponding major key in the circle of fifths. The length of each vector reflects the multiplicity (i.e. number of occurrences) of the pitch class in a musical piece or its fragment. The paper presents the theoretical background, examples explaining the essence of the idea and the results of the conducted tests which confirm the effectiveness of the proposed algorithm for finding the key based on the analysis of the music signature. The developed method was compared with the key-finding algorithms using Krumhansl-Kessler, Temperley and Albrecht-Shanahan profiles. The experiments were performed on the set of Bach preludes, Bach fugues and Chopin preludes.