#Music spectrograph windows windows#

Adaptive resolution will also allow good frequency resolution in stationary signal parts and good time resolution around transients leading to less time smearing artifacts.

Proper selection of time-frequency resolution will result in better energy compaction in the transform domain, which is always desirable for noise reduction. We suggest using one of the strategies described in section 4 to adapt the time-frequency resolution of the filter bank. Another problem is general pre-echo associated with the modification of STFT coefficients at poor time resolution. Spectral subtraction with poor time resolution is not able to suppress noise before transient onsets since the part of the transient falls into the window and raises the coefficient magnitude preventing attenuation (Figs.

#Music spectrograph windows windows#

However good frequency resolution requires long STFT windows leading to poor time resolution. Good frequency resolution also leads to stronger possible noise attenuation due to lower noise power per STFT bin. Good frequency resolution of the STFT filter bank allows separation of closely spaced noise and signal harmonics. A typical filter bank for spectral subtraction is based on the STFT. In this paper, we will not discuss details of spectral subtraction methods, but rather show how modification of a filter bank can improve the quality of the result by reducing artifacts specific to filter banks. Then the inverse filter bank reconstructs the cleaned signal. This algorithm transforms the noisy signal with a filter bank and attenuates coefficients that are supposedly part of the noise, using a-priori knowledge of the noise spectrum. Most noise reduction methods for additive stationary noises in audio are based upon the spectral subtraction algorithm. In spite of the increased computational complexity compared to a single-resolution STFT, both algorithms allow real-time implementation.

The resulting audio signal shows significant reduction of time smearing of transients and at the same time good frequency resolution allowing effective suppression of tonal noise or extraction of the center channel. We run several instances of single-resolution processors and adaptively combine their results in the time-frequency plane using one of the suggested strategies. In this section, we show how filter banks with adaptive time-frequency resolution can be applied to improve the quality of several audio processing algorithms: the spectral subtraction algorithm for noise reduction and the center channel extraction algorithm. These tests show that the proposed adaptive approach to the calculation of spectrograms allows a spectrogram to display more useful details and musical events with better precision. In the high-frequency area we are able to preserve the sharpness of drum onsets and resolve closely spaced guitar harmonics. Again, the adaptive spectrogram is able to resolve low-frequency harmonics, and it avoids the smearing of bass drum hits (at 0.3 and 1.0 seconds). 6 displaying a piece of rock music with vocal, bass, drums, guitars, flute and violin. Another example of our adaptive spectrograms is given in Fig. The adaptiveness of the time-frequency resolution also enables us to use better frequency resolution at high frequencies to resolve closely spaced guitar overtones above 3 kHz. 11) fixes both of these problems: the low- frequency resolution is increased, and drum onsets are displayed more sharply due to better local time resolution. At the same time the temporal resolution at high frequencies is not enough to sharply localize onsets of the drums. 10) lacks low-frequency resolution and is unable to separate bass notes of the cello and guitar. Our next example is a piece of folk music with flute, cello, guitar and percussive drums. 9) leading to less frequency spreading of the slow tone decay and better time localization of the tone onset. However our adaptive spectrogram is able to locally select the time-frequency resolution which minimizes smear both in time and frequency (Fig. By varying the resolution of the spectrogram, we can make either the horizontal line (the decaying tone) or the vertical line (the transient attack) thinner, but not both at once. Since the tone onset is abrupt, it contains a transient energy burst spreading outside of the 1 kHz band, as shown by the conventional spectrogram (Fig. The first test signal consisted of an artificially generated 1 kHz tone with a sharp fade-in lasting 2 ms and a smooth decay lasting 600 ms (Fig. We have conducted simulations to compare the look and usefulness of conventional STFT spectrograms and adaptive-resolution spectrograms. k is the normalization constant selected so that the sum of all w r is 1, and ε is a small constant preventing division by 0.