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

Time-Frequency Trade-offs for Audio Source Separation with Binary Masks