{"id":152066,"date":"2004-11-01T00:00:00","date_gmt":"2004-11-01T00:00:00","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/msr-research-item\/mixing-with-mozart\/"},"modified":"2018-10-16T20:02:08","modified_gmt":"2018-10-17T03:02:08","slug":"mixing-with-mozart","status":"publish","type":"msr-research-item","link":"https:\/\/www.microsoft.com\/en-us\/research\/publication\/mixing-with-mozart\/","title":{"rendered":"Mixing with Mozart"},"content":{"rendered":"<div class=\"asset-content\">\n<p>A variety of tools exist in hardware and software for mixing dance music. These work by estimating the &#8220;beats-per-minute&#8221; count of music with heavy beats. These tools aid a DJ in finding the appropriate speed change and time shift to smoothly combine or transition between two pieces of music. In this work, we present a method for finding these alignments and combining a wider class of songs with dance music (e.g., Mozart with techno). We do this by jointly optimizing the energy alignment of both signals instead of attempting to detect individual beats\/tempos. Though computationally intensive if naively computed, we introduce an approximation to greatly speed up the evaluation of the tens of thousands of possible matches. This results in a set of a few top choices for alignment parameters. We also develop a measure of the quality of the match to help assess whether the best alignment is actually a good fit. We show a variety of results demonstrating the use of this method.<\/p>\n<\/div>\n<p><!-- .asset-content --><\/p>\n","protected":false},"excerpt":{"rendered":"<p>A variety of tools exist in hardware and software for mixing dance music. These work by estimating the &#8220;beats-per-minute&#8221; count of music with heavy beats. These tools aid a DJ in finding the appropriate speed change and time shift to smoothly combine or transition between two pieces of music. In this work, we present a 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