Main Article Content
Abstract
Audio analysis is fast becoming a requirement of digital media for analysing multiple frequencies of sound at a time and also reduce the background noise.The urban sound challenge is increasing day by day and the problem is meant to introduce for audio analysis and processing in the usual classification scenario. Model is implemented on the basis of keras framework and librosalibrary. Keras is capable of running of the algorithm tensorflow. Kerascan be described as an interface rather than a standalone machine learning tools. Librosa is one of the python library for music and audio analysis. It helps us with necessary music information retrieval systems.
The authorcollect the database and use the data and also use the graph for a better understanding of audio data analysis.
Keywords
Article Details
This work is licensed under a Creative Commons Attribution 4.0 International License.
The copyright for all manuscripts/ documents belongs to the authors.
References
- Gideon mendels,”how to apply deep learning and machine learning method in audio analysis”, Nov,2019
- Acton, Ciaran,” analysis of variance(ANOVA)”,2009
- Mitchell,”machine learning”,1997
- Bishop,C.M,”pattern recognition and machine learning”,2006
- Schmidhuber, “deep learning in neural network”,2015
- FaizanShaikh,”getting started with audio data analysis using deep learning”, August,2017
- G.W.Smith,” The machine as Artist”,April,2017
- Shen,yelong,”a latent semantic model with convolution-pooling structure for information retrieval”,October,2013
- Jotikasingh,”introduction to audio processing”,September,2019
- A.Huang, and R.Wu,”deep learning for music”
- N.Jaitly and G.Hinton,”learning a better representation og speech soundwaves using neural networks”,2011