Boosted Audio Recall for Music Matching

Music is frequently modified from an original version before uploading to music-sharing or video-sharing websites or social media networks. For example, the music can be remixed, have voice-overs added, or have other edits. In several use cases, e.g., search, deduplication, ensuring fair use, etc. it is of interest to determine if an uploaded audio track is substantially similar to existing audio tracks in a database. However, modifications made to the original version can in some instances be enough that a match is not obtained with any track in the reference database even when the tracks match substantially. This disclosure describes neural network based techniques to ignore modifications, e.g., voice-overs, from an audio track such that a match, if any, with a reference audio track is easier to detect

Improving Image Search by Augmenting Image Queries

Matching image or video content to other content is an important requirement for content hosting platforms. A common mechanism is to construct an index of known content, e.g., that include multi-dimensional embeddings generated from the content, and match new content against the index. The precision and recall of such techniques require a high quality fingerprint, and tradeoffs between recall performance and the cost of filtering out false positives. This disclosure describes improvements to content matching techniques that generate multiple transformations of the input content, look up each transformation in the index, and limit detection of false positives or other downstream analysis to content that has at least a threshold number of matches. Performance improvements in the recall vs. cost tradeoff are obtained due to the shape of the volume in the embedding space is no longer spherical, and instead, including many smaller spheres around the different transformed versions

Learning probabilistic relational planning rules

To learn to behave in highly complex domains, agents must represent and learn compact models of the world dynamics. In this paper, we present an algorithm for learning probabilistic STRIPS-like planning operators from examples. We demonstrate the effective learning of rule-based operators for a wide range of traditional planning domains

Finding Match Avoidance Attempts At Scale With Video Expansion

An important objective of user-generated content platforms such as audio/video hosting or streaming platforms is to ensure that content that is available via their platforms is authorized for use, e.g., is provided by the true owner or with due permission of the true owner. To ensure that unauthorized content is not made available, such platforms match uploaded videos against a repository of reference (original) videos. To avoid video content being matched, content uploaders utilize constantly evolving new content transformation strategies when uploading unauthorized content. This disclosure describes automated techniques that help speed up and scale the collection of training examples of recent techniques of content transformations designed to bypass match detection procedures. These include synthetic generation (automatically generating content examples similar to match avoiding content) and scaled up mining and filtering (which includes performing searches for other content that is similar to match avoiding content on some dimension and filtering such content using high performance matching algorithms) to detect other examples of similar match avoiding content. The corpus of data generated by the described techniques can be used to train and validate a new version of matching procedures that is robust to the recent match avoidance attempts

Decayed MCMC filtering

Filtering—estimating the state of a partially observable Markov process from a sequence of observations—is one of the most widely studied problems in control theory, AI, and computational statistics. Exact computation of the posterior distribution is generally intractable for large discrete systems and for nonlinear continuous systems, so a good deal of effort has gone into developing robust approximation algorithms. This paper describes a simple stochastic approximation algorithm for filtering called decayed MCMC. The algorithm applies Markov chain Monte Carlo sampling to the space of state trajectories using a proposal distribution that favours flips of more recent state variables. The formal analysis of the algorithm involves a generalization of standard coupling arguments for MCMC convergence. We prove that for any ergodic underlying Markov process, the convergence time of decayed MCMC with inversepolynomial decay remains bounded as the length of the observation sequence grows. We show experimentally that decayed MCMC is at least competitive with other approximation algorithms such as particle filtering.

Tracking many objects with many sensors

Keeping track of multiple objects over time is a problem that arises in many real-world domains. The problem is often complicated by noisy sensors and unpredictable dynamics. Previous work by Huang and Russell, drawing on the data association literature, provided a probabilistic analysis and a threshold-based approximation algorithm for the case of multiple objects detected by two spatially separated sensors. This paper analyses the case in which large numbers of sensors are involved. We show that the approach taken by Huang and Russell, who used pairwise sensor-based appearance probabilities as the elementary probabilistic model, does not scale. When more than two observations are made, the objects ' intrinsic properties must be estimated. These provide the necessary conditional independencies to allow a spatial decomposition of the global probability model. We also replace Huang and Russell's threshold algorithm for object identification with a polynomial-time approximation scheme based on Markov chain Monte Carlo simulation. Using sensor data from a freeway traffic simulation, we show that this allows accurate estimation of long-range origin/destination information even when the individual links in the sensor chain are highly unreliable.