  This paper thoroughly analyses the effect of different input representations
on polyphonic multi-instrument music transcription. We use our own GPU based
spectrogram extraction tool, nnAudio, to investigate the influence of using a
linear-frequency spectrogram, log-frequency spectrogram, Mel spectrogram, and
constant-Q transform (CQT). Our results show that a $8.33$% increase in
transcription accuracy and a $9.39$% reduction in error can be obtained by
choosing the appropriate input representation (log-frequency spectrogram with
STFT window length 4,096 and 2,048 frequency bins in the spectrogram) without
changing the neural network design (single layer fully connected). Our
experiments also show that Mel spectrogram is a compact representation for
which we can reduce the number of frequency bins to only 512 while still
keeping a relatively high music transcription accuracy.
