33,480 research outputs found
Signal Enhancement by Single Channel Source Separation
Most gadgets and electronics devices are commonly equipped with single microphone only. This is difficult task in source separation world which traditionally required more sensors than sources to achieve better performance. In this paper we evaluated single channel source separation to enhance target signal from interred noise. The method we used is non-negative matrix factorization (NMF) that decompose signal into its components and find the matched signal to target speaker. As objective evaluation, coherence score is used to measure the perceptual similarity from enhanced to original one. It show the extracted has 0.5 of average coherence that shows medium correlation between both signals
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The influences of environmental conditions on source localisation using a single vertical array and their exploitation through ground effect inversion
The performance of microphone arrays outdoors is influenced by the environmental conditions. Numerical simulations indicate that, while horizontal arrays are hardly affected, direction-of-arrival (DOA) estimation with vertical arrays becomes biased in presence of ground reflections and sound speed gradients. Turbulence leads to a huge variability in the estimates by reducing the ground effect. Ground effect can be exploited by combining classical source localization with an appropriate propagation model (ground effect inversion). Not only does this allow the source elevation and range to be determined with a single vertical array but also it allows separation of sources which can no longer be distinguished by far field localization methods. Furthermore, simulations provide detail of the achievable spatial resolution depending on frequency range, array size and localization algorithm and show a clear advantage of broadband processing. Outdoor measurements with one or two sources confirm the results of the numerical simulations
Signal Enhancement by Single Channel Source Separation
Most gadgets and electronics devices are commonly equipped with single microphone only. This is difficult task in source separation world which traditionally required more sensors than sources to achieve better performance. In this paper we evaluated single channel source separation to enhance target signal from interred noise. The method we used is non-negative matrix factorization (NMF) that decompose signal into its components and find the matched signal to target speaker. As objective evaluation, coherence score is used to measure the perceptual similarity from enhanced to original one. It show the extracted has 0.5 of average coherence that shows medium correlation between both signals
Pemisahan Banyak Sumber Suara Mesin Menggunakan Independent Component Analysis (Ica) Untuk Deteksi Kerusakan
Pemeliharaan kondisi mesin di industri membutuhkan kecepatan dan kemudahan, salah satu metodenya adalah dengan analisis getaran. Getaran mesin menyebabkan pola suara yang diemisikan mesin, di mana suara mesin satu bercampur dengan mesin lainnya. Blind Source Separation (BSS) merupakan teknik memisahkan sinyal campuran berdasarkan sifat kebebasan statistik antar sumber. Melalui simulasi dengan beberapa motor dan susunan mikrofon sebagai sensor, didapatkan data suara campuran dari beberapa motor yang terekam melalui tiap mikrofon. Intensitas sinyal yang diterima mikrofon berbeda satu sama lain, tergantung pada jarak dan sudut datangnya. Tujuan penelitian ini adalah untuk memisahkan sinyal campuran dari tiap mikrofon sehingga didapatkan sinyal estimasi sumber untuk mendeteksi kerusakan motor. Berdasarkan hasil penelitian, diperoleh pemisahan sinyal terbaik dalam Time-Domain ICA. Sinyal estimasi tersebut dianalisis untuk menentukan kondisi kerusakan mesin berdasarkan pola frekuensi sesaatnya.
Maintenance of engine conditionin the industry requires speed and convenience, one of the method is by vibration analysis. Machine’s vibration causes the machine emitted sound pattern, in which an engine sound mixed with other machine’s. Blind Source Separation (BSS) is a technique to separate mixed signals based on the statistical independence properties between the sources. Through simulation with several motors and the composition of the microphones as the sensor, noise mixture data obtained from some motors recorded by each microphone. The signal intensity received by microphone are different from each other, depending on the distance and angle of arrival. The purpose of this study is to separate the mixed signals from each microphone to obtain estimation of the signal source to detect the motor damage . Based on the research, obtained the best signal separation in the Time-Domain ICA. Signal estimation is analyzed to determine the condition of an engine failure patterns based on instantaneous frequency
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