Seattle conference on scalability

Posted by Amanda Camp, Software Engineer

We care a lot about scalability at Google. An algorithm that works only on a small scale doesn't cut it when we are talking global access, millions of people, millions of search queries. We think big and love to talk about big ideas, so we're planning our first ever conference on scalable systems. It will take place on June 23 at our Seattle office. Our goal: to create a collegial atmosphere for participants to brainstorm different ways to build the robust systems that can handle, literally, a world of information.

If you have a great new idea for handling a growing system or an innovative approach to scalability, we want to hear from you. Send a short note about who you are and a description of your 45-minute talk in 500 words or less to scalabilityconf@google.com by Friday, April 20.

With your help, we can create an exciting event that brings together great people and ideas. (And by the way, we'll bring the food.) If you'd like to attend but not speak, we'll post registration details later.

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Hear, here. A Sample of Audio Processing at Google.

Posted by Shumeet Baluja, Michele Covell, Pedro Moreno & Eugene Weinstein

Text isn't the only source of information on the web! We've been working on a variety of projects related to audio and visual recognition. One of the fundamental constraints that we have in designing systems at Google is the huge amounts of data that we need to process rapdily. A few of the research papers that have come out of this work are shown here.

In the first pair of papers, to be presented at the 2007 International Conference on Acoustics, Speech and Signal Processing (Waveprint Overview, Waveprint-for-Known-Audio), we show how computer vision processing techniques, combined with large-scale data stream processing, can create an efficient system for recognizing audio that has been degraded by various means such as cell phone playback, lossy compression, echoes, time-dilation (as found on the radio), competing noise, etc.

It is also fun and surprising to see how often in research the same problem can be approached from a completely different perspective. In the third paper to be presented at ICASSP-2007 (Music Identification with WFST) we explore how acoustic modeling techniques commonly used in speech recognition, and finite state transducers used to represent and search large graphs, can be used in the problem of music identification. Our approach learns a common alphabet of music sounds (which we call music-phones) and represents large song collections as a big graph where efficient search is possible.

Perhaps one of the most interesting aspects of audio recognition goes beyond the matching of degraded signals, and instead attempts to capture meaningful notions of similarity. In our paper presented at the International Conference on Artificial Intelligence (Music Similarity), we describe a system that learns relevant similarities in music signals, while maintaining efficiency by using these learned models to create customized hashing functions.

We're extending these pieces of work in a variety of ways, not only in the learning algorithms used, but also the application areas. If you're interested in joining google research and working on these projects, be sure to drop us a line.

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