TAILIEUCHUNG - An Industrial-Strength Audio Search Algorithm

Even when prohibited people are flagged by the system, they’re almost never stopped. In 2010, out of 76,000 denied purchasers, only 13 were successfully prosecuted under federal law for illegally attempting to buy a gun. That universal failure endangers us all! The mentally ill are also not part of their universe. For the last 20 years, since the NICS system came on line, government has failed to include records of those judged mentally ill by a court of law. If we can’t even get those records into the system, are we truly to believe. | An Industrial-Strength Audio Search Algorithm Avery Li-Chun Wang avery@ Shazam Entertainment Ltd. USA 2925 Ross Road Palo Alto CA 94303 United Kingdom 375 Kensington High Street 4th Floor Block F London W14 8Q We have developed and commercially deployed a flexible audio search engine. The algorithm is noise and distortion resistant computationally efficient and massively scalable capable of quickly identifying a short segment of music captured through a cellphone microphone in the presence of foreground voices and other dominant noise and through voice codec compression out of a database of over a million tracks. The algorithm uses a combinatorially hashed time-frequency constellation analysis of the audio yielding unusual properties such as transparency in which multiple tracks mixed together may each be identified. Furthermore for applications such as radio monitoring search times on the order of a few milliseconds per query are attained even on a massive music database. 1 Introduction Shazam Entertainment Ltd. was started in 2000 with the idea of providing a service that could connect people to music by recognizing music in the environment by using their mobile phones to recognize the music directly. The algorithm had to be able to recognize a short audio sample of music that had been broadcast mixed with heavy ambient noise subject to reverb and other processing captured by a little cellphone microphone subjected to voice codec compression and network dropouts all before arriving at our servers. The algorithm also had to perform the recognition quickly over a large database of music with nearly 2M tracks and furthermore have a low number of false positives while having a high recognition rate. This was a hard problem and at the time there were no algorithms known to us that could satisfy all these constraints. We eventually developed our own technique that met all the operational constraints 1 . We have deployed the algorithm to scale in our .

• Cơ sở dữ liệu
• Chromatography Equipped
• Headspace (GC–HS)
• Wine Technology
• Odorant Series
• Analytical Tool
• Biological Ageing
• Major Volatile
• Compounds Analysis
• Produced
• Mezcal Fermentation
• Chromatography
• Equipped Headspace