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 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 to the calculated values.
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