There are 0 repository under zero-crossing-rate-variation topic.
Voice Activity Detection in speech signals using short time energy and zero-crossings rate
Bali has a diversity of arts that has been recognized by the world, where one of the most famous Balinese arts is the Karawitan art, especially the Kendang Tunggal instrument. Notation documentation or more commonly known as music transcription, can make learning a song easier, and in the case of this research, it makes it easier to learn to play the Kendang Tunggal instrument. The first approach method used to document a kendang tunggal song is onset detection. Onset is when the signal experiences an attack period, which helps segment the sound color of the drum instrument. The segmented kendang tunggal sound color classification uses the Backpropagation algorithm with several features of the frequency domain and time domain as a characteristic of the sound color. Then the kendang tunggal song is revived into a synthetic sound with the Mel Spectral Approximation filter. Based on the research, the optimal parameter for drum sound color segmentation with onset detection is the hop size 110 with normalization of the features on its onset detection function. The optimal backpropagation architecture obtained with a learning rate of 0.9, neurons 10, and epoch 2000 produces an accuracy of 60.85%. The synthesis method using the Mel Log Spectrum Approximation can make synthetic sounds similar to kendang songs with an accuracy of 83.33%
🎙Audio analysis - a field that includes automatic speech recognition(ASR)🎛, digital signal processing🎚, and music classification🎶, tagging📻, and generation🎧 - is a 🎼growing subdomain of 🎵deep learning applications🎤
Machine Learning based Cough-Detection from Audiorecordings by using MFCCs, PCA and One-Class-SVM
In this repository, we first read the file of a signal in Matlab and then calculate for that signal the energy value of the short time, the magnitude of the short time, and the Zero crossing rate on the short time. After seeing the output of the above values, we check the autocorrelation for vowels and non-vowels in our selected part of the file.