TabTracking

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Audio recording presents significant challenges due to the complex nature of music, especially when compared to other signals. This complexity becomes even more pronounced in polyphonic music, where multiple instruments and voices overlap. Despite these challenges, numerous techniques have been developed to process music effectively for audio applications. TabTracking investigates methods for transcribing music into written form, with a focus on capturing the sound of an electric guitar. Techniques such as FFT, STFT, spectrograms, scalograms, chroma representation, peak detection, and machine learning approaches are analyzed. In addition, a hardware implementation in HDL is presented that uses an FPGA. A core aspect of this application is the generation of guitar tabs, a numerical notation system that indicates which string and fret was played on the guitar. Possible use cases for this work include the digitization of guitar performances, where musicians can record songs directly by playing, rather than manually entering tablature, or a jam session, where spontaneous musical ideas can be captured through informal, collaborative music-making with other musicians.

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Pagina's: 148, Paperback, LAP LAMBERT Academic Publishing