| Summary: | 碩士 === 國立清華大學 === 資訊工程學系 === 101 === The goal of this research is to improve the current audio fingerprinting technique. Audio fingerprinting is a fast and convenient music retrieval method that allows a user to retrieve an intended song and related information by recording a portion of the song under a noisy environment.
In order to improve the recognition rate of our system, we classify the queried segment into one of the two classes: easy or difficult to find the intended song. The recognition mechanism is as follows. Before the queried segment is recognized, we adopt SVM as our classifier to classify the queried segment. Depending on its class, we conduct 4 or 8 times of landmark finding on this query and then perform the matching step as usual. The recognition rate by using our method is 84.18%, which is close to 84.28% by using 8 times of landmarks finding, and the matching time is also reduced by 2% of the time required by using 8 times of landmarks finding.
In addition, we employ a verification mechanism using confidence measure in our audio fingerprinting system to determine if the query is in our database or not. If the confidence result is lower than a certain threshold, our system rejects this query. When we set the matched landmark count per second as 1.5, we can filter about 86% of queried segments which are not in our database.
At last, if the matched landmark count of the user-defined duration of the queried segment is greater than the confidence threshold, our system returns the result directly. Otherwise, the system extends the duration of the queried segment for searching and matching. Therefore, we divide a query into two parts with equal length to conduct the experiment. To solve the problem of missing landmarks on edge between two parts, we overlap 15 frames towards the front for the second part of the query segment. And we also find the landmarks forwards only. This effectively solves the problem of finding duplicate landmarks of the former segment when finding landmarks bidirectionally. Comparing to the original method, this method achieves a 21% reduction in response time and a 2% improvement in recognition rate.
Keywords: music retrieval, audio fingerprinting, landmark, SVM, confidence measure, segmental music query
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