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The usual process for data sonification is directing digital media of a dataset through a software synthesizer and into a digital-to-analog converter to produce sound for humans to experience. [ 1 ] [ 2 ] [ 3 ] Benefits to interpreting data through sonification include accessibility, pattern recognition, education, and artistic expression.
Analysis can often require some summarising, [2] and for music (as with many other forms of data) this is achieved by feature extraction, especially when the audio content itself is analysed and machine learning is to be applied. The purpose is to reduce the sheer quantity of data down to a manageable set of values so that learning can be ...
Computer audition (CA) or machine listening is the general field of study of algorithms and systems for audio interpretation by machines. [1] [2] Since the notion of what it means for a machine to "hear" is very broad and somewhat vague, computer audition attempts to bring together several disciplines that originally dealt with specific problems or had a concrete application in mind.
But today, with digital audio, any piece of data can be mapped into sound. Kyma was developed by Carla Scaletti, a composer and sound engineer based in Urbana-Champaign. Its original purpose was ...
Deep learning speech synthesis refers to the application of deep learning models to generate natural-sounding human speech from written text (text-to-speech) or spectrum . Deep neural networks are trained using large amounts of recorded speech and, in the case of a text-to-speech system, the associated labels and/or input text.
Audio mining is a technique by which the content of an audio signal can be automatically analyzed and searched. It is most commonly used in the field of automatic speech recognition , where the analysis tries to identify any speech within the audio.
Machine learning and data mining often employ the same methods and overlap significantly, but while machine learning focuses on prediction, based on known properties learned from the training data, data mining focuses on the discovery of (previously) unknown properties in the data (this is the analysis step of knowledge discovery in databases).
The difference between data analysis and data mining is that data analysis is used to test models and hypotheses on the dataset, e.g., analyzing the effectiveness of a marketing campaign, regardless of the amount of data. In contrast, data mining uses machine learning and statistical models to uncover clandestine or hidden patterns in a large ...
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