  The short-time Fourier transform (STFT) provides the foundation of
binary-mask based audio source separation approaches. In computing a
spectrogram, the STFT window size parameterizes the trade-off between time and
frequency resolution. However, it is not yet known how this parameter affects
the operation of the binary mask in terms of separation quality for real-world
signals such as speech or music. Here, we demonstrate that the trade-off
between time and frequency in the STFT, used to perform ideal binary mask
separation, depends upon the types of source that are to be separated. In
particular, we demonstrate that different window sizes are optimal for
separating different combinations of speech and musical signals. Our findings
have broad implications for machine audition and machine learning in general.
