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    Signal Enhancement by Single Channel Source Separation

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    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

    Signal Enhancement by Single Channel Source Separation

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    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

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    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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