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Floating-Point Encoding for Transcription of High-Fidelity Audio Signals
For professional quality transcription of audio signals, a floating-point representation can give substantial savings over a straight binary representation. This paper shows, based on psychoacoustic data of masking of noise, how floating-point mantissa length, radix value, and exponent length can be arrived at. Listening tests, using real-time computer-generated music, yielded excellent agreement with the calculated values.
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