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Query by Humming (QBH) is an system to provide a user with the song(s) which the user hums to the system. Current QBH method requires the extraction of onset and pitch information in order to track similarity with various versions of different songs. However, we here focus on detecting precise onsets only and use them to build a QBH system which is...
Contexts in source publication
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... whole process of binning is called Quantization. An illustration of the process is shown in Figure 1. ...
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... perform the above analysis for Spectral Dissimilarity also. Figure 10 shows the values of the Spectral Dissimilarity function obtained for window length of ω = 4096 samples, for reasons mentioned earlier. Note that, the hopsize for this detection algorithm should be set to high values (2048 samples in our study), as the changes in the function is very rapid and we do not want to detect any unnecessary changes in spectrum. ...
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... that, the hopsize for this detection algorithm should be set to high values (2048 samples in our study), as the changes in the function is very rapid and we do not want to detect any unnecessary changes in spectrum. Also, the effect of the choice of neighbouring points with mean based and quartile based thresholding is shown in Figure 11 and Figure 12 respectively. We decide to use 4 points to both sides for comparison in peak detection algorithm. ...
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... that, the hopsize for this detection algorithm should be set to high values (2048 samples in our study), as the changes in the function is very rapid and we do not want to detect any unnecessary changes in spectrum. Also, the effect of the choice of neighbouring points with mean based and quartile based thresholding is shown in Figure 11 and Figure 12 respectively. We decide to use 4 points to both sides for comparison in peak detection algorithm. ...
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... Dominant Spectral Dissimilarity detection function with the same window length ω = 4096 and hopsize being 2048, we also perform a similar exercise. Figure 13 shows different values of detection function for the first hummed song. Again, it is seen that the onsets are reflected as much higher peaks in the detection function. ...
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... it is seen that the onsets are reflected as much higher peaks in the detection function. Figure 14 and 15 summarizes the performance of the detector under various different neighbour setups along with different thresholding criterion. From the outputs, it seems more reasonable to work with 2 neighbours on both sides, with mean based thresholding criterion. ...
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... on the optimal setting for Local Energy detector, we compute the detection function and obtain the peaks. Figure 17 shows how bad the performance of Local Energy Detector is affected due to the presence of meends. As we see, there are lots of false negatives in the detected onsets, which should cause a detrimental effect to the performance of the searching algorithm. ...
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... Spectral Dissimilarity Detection function with its optimal setting, we obtain a much better values of detected onsets, as seen from Figure 18. Also note that, there is one possible false negative during the starting of the song. ...
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... we apply Dominant Spectral Dissimilarity detection function on this hummed song at its optimal hyperparameter setups. As seen from Figure 19, this algorithm performs similar to the Spectral Dissimilarity detection function. Note that, the onset that Spectral Dissimilarity failed to detect, has been detected by this algorithm, however, paying a cost of missed detection of another onset. ...
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... perform Local energy detection function on this hummed version of the song. From Figure 21, it is evident that many true onsets remain undetected by the detection algorithm. Note that, as Table 3 shows, this poor performance of the detection function affects the searching algorithm, which fails to identify the song correctly. ...
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... whole process of binning is called Quantization. An illustration of the process is shown in Figure 1. ...
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... perform the above analysis for Spectral Dissimilarity also. Figure 10 shows the values of the Spectral Dissimilarity function obtained for window length of ω = 4096 samples, for reasons mentioned earlier. Note that, the hopsize for this detection algorithm should be set to high values (2048 samples in our study), as the changes in the function is very rapid and we do not want to detect any unnecessary changes in spectrum. ...
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... that, the hopsize for this detection algorithm should be set to high values (2048 samples in our study), as the changes in the function is very rapid and we do not want to detect any unnecessary changes in spectrum. Also, the effect of the choice of neighbouring points with mean based and quartile based thresholding is shown in Figure 11 and Figure 12 respectively. We decide to use 4 points to both sides for comparison in peak detection algorithm. ...
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... that, the hopsize for this detection algorithm should be set to high values (2048 samples in our study), as the changes in the function is very rapid and we do not want to detect any unnecessary changes in spectrum. Also, the effect of the choice of neighbouring points with mean based and quartile based thresholding is shown in Figure 11 and Figure 12 respectively. We decide to use 4 points to both sides for comparison in peak detection algorithm. ...
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... Dominant Spectral Dissimilarity detection function with the same window length ω = 4096 and hopsize being 2048, we also perform a similar exercise. Figure 13 shows different values of detection function for the first hummed song. Again, it is seen that the onsets are reflected as much higher peaks in the detection function. ...
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... it is seen that the onsets are reflected as much higher peaks in the detection function. Figure 14 and 15 summarizes the performance of the detector under various different neighbour setups along with different thresholding criterion. From the outputs, it seems more reasonable to work with 2 neighbours on both sides, with mean based thresholding criterion. ...
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... on the optimal setting for Local Energy detector, we compute the detection function and obtain the peaks. Figure 17 shows how bad the performance of Local Energy Detector is affected due to the presence of meends. As we see, there are lots of false negatives in the detected onsets, which should cause a detrimental effect to the performance of the searching algorithm. ...
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... Spectral Dissimilarity Detection function with its optimal setting, we obtain a much better values of detected onsets, as seen from Figure 18. Also note that, there is one possible false negative during the starting of the song. ...
Context 19
... we apply Dominant Spectral Dissimilarity detection function on this hummed song at its optimal hyperparameter setups. As seen from Figure 19, this algorithm performs similar to the Spectral Dissimilarity detection function. Note that, the onset that Spectral Dissimilarity failed to detect, has been detected by this algorithm, however, paying a cost of missed detection of another onset. ...
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... perform Local energy detection function on this hummed version of the song. From Figure 21, it is evident that many true onsets remain undetected by the detection algorithm. Note that, as Table 3 shows, this poor performance of the detection function affects the searching algorithm, which fails to identify the song correctly. ...
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