The processing of sound signals is significantly improved recently.Technique for sound signal processing focusing on music beyond speech area is getting attention due to the development of deep learning techniques.Thi...The processing of sound signals is significantly improved recently.Technique for sound signal processing focusing on music beyond speech area is getting attention due to the development of deep learning techniques.This study is for analysis and process of music signals to generate tow-dimensional tabular data and a new music.For analysis and process part,we represented normalized waveforms for each of input data via frequency domain signals.Then we looked into shorted segment to see the difference wave pattern for different singers.Fourier transform is applied to get spectrogram of the music signals.Filterbank is applied to represent the spectrogram based on the human ear instead of the distance on the frequency dimension,and the final spectrogram has been plotted by Mel scale.For generating part,we created two-dimensional tabular data for data manipulation.With the 2D data,any kind of analysis can be done since it has digit values for the music signals.Then,we generated a new music by applying LSTM toward the song audience preferred more.As the result,it has been proved that the created music showed the similar waveforms with the original music.This study made a step forward for music signal processing.If this study expands further,it can find the pattern that listeners like so music can be generated within favorite singer’s voice in the way that the listener prefers.展开更多
文摘The processing of sound signals is significantly improved recently.Technique for sound signal processing focusing on music beyond speech area is getting attention due to the development of deep learning techniques.This study is for analysis and process of music signals to generate tow-dimensional tabular data and a new music.For analysis and process part,we represented normalized waveforms for each of input data via frequency domain signals.Then we looked into shorted segment to see the difference wave pattern for different singers.Fourier transform is applied to get spectrogram of the music signals.Filterbank is applied to represent the spectrogram based on the human ear instead of the distance on the frequency dimension,and the final spectrogram has been plotted by Mel scale.For generating part,we created two-dimensional tabular data for data manipulation.With the 2D data,any kind of analysis can be done since it has digit values for the music signals.Then,we generated a new music by applying LSTM toward the song audience preferred more.As the result,it has been proved that the created music showed the similar waveforms with the original music.This study made a step forward for music signal processing.If this study expands further,it can find the pattern that listeners like so music can be generated within favorite singer’s voice in the way that the listener prefers.
