Automatic cover detection -- the task of finding in an audio database all the covers of one or several query tracks -- has long been seen as a challenging theoretical problem in the MIR community and as an acute practical problem for authors and composers societies. Original algorithms proposed for this task have proven their accuracy on small datasets, but are unable to scale up to modern real-life audio corpora. On the other hand, faster approaches designed to process thousands of pairwise comparisons resulted in lower accuracy, making them unsuitable for practical use. In this work, we propose a neural network architecture that is trained to represent each track as a single embedding vector. The computation burden is therefore left to the embedding extraction -- that can be conducted offline and stored, while the pairwise comparison task reduces to a simple Euclidean distance computation. We further propose to extract each track's embedding out of its dominant melody representation, obtained by another neural network trained for this task. We then show that this architecture improves state-of-the-art accuracy both on small and large datasets, and is able to scale to query databases of thousands of tracks in a few seconds.
Another type of method has been proposed to alleviate the cost of the comparison function and to shift the burden to the audio features extraction function -- which can be done offline and stored. The general principle is to encode each audio track as a single scalar or vector -- its embedding -- and to reduce the similarity computation to a simple Euclidean distance between embeddings. Originally, embeddings were for instance computed as a single hash encoding a succession of pitch landmarks @cite_43 , or as a vector obtained by PCA dimensionality reduction of a chromagram's 2D-DFT @cite_32 or with locality-sensitive hashing of melodic excerpts @cite_25 .
@cite_43 @cite_25 @cite_32
2097461116 1486009449
Searching audio collections using high-level musical descriptors is a difficult problem, due to the lack of reliable methods for extracting melody, harmony, rhythm, and other such descriptors from unstructured audio signals. In this paper, we present a novel approach to melody-based retrieval in audio collections. Our approach supports audio, as well as symbolic queries and ranks results according to melodic similarity to the query. We introduce a beat-synchronous melodic representation consisting of salient melodic lines, which are extracted from the analyzed audio signal. We propose the use of a 2D shift-invariant transform to extract shift-invariant melodic fragments from the melodic representation and demonstrate how such fragments can be indexed and stored in a song database. An efficient search algorithm based on locality-sensitive hashing is used to perform retrieval according to similarity of melodic fragments. On the cover song detection task, good results are achieved for audio, as well as for symbolic queries, while fast retrieval performance makes the proposed system suitable for retrieval in large databases. Learning to hash involves learning hash functions from a set of images for embedding high-dimensional visual descriptors into a similarity-preserving low-dimensional Hamming space. Most of existing methods resort to a single representation of images, that is, only one type of visual descriptors is used to learn a hash function to assign binary codes to images. However, images are often described by multiple different visual descriptors (such as SIFT, GIST, HOG), so it is desirable to incorporate these multiple representations into learning a hash function, leading to multi-view hashing. In this paper we present a sequential spectral learning approach to multi-view hashing where a hash function is sequentially determined by solving the successive maximization of local variances subject to decorrelation constraints. We compute multi-view local variances by α-averaging view-specific distance matrices such that the best averaged distance matrix is determined by minimizing its α-divergence from view-specific distance matrices. We also present a scalable implementation, exploiting a fast approximate k-NN graph construction method, in which α-averaged distances computed in small partitions determined by recursive spectral bisection are gradually merged in conquer steps until whole examples are used. Numerical experiments on Caltech-256, CIFAR-20, and NUS-WIDE datasets confirm the high performance of our method, in comparison to single-view spectral hashing as well as existing multi-view hashing methods. In many situations, such as in biometric applications, there is need to encrypt and “hide” data, while simultaneously permitting restricted computations on them. We present a method to securely determine the 2 distance between two signals if they are close enough. This method relies on a locality sensitive hashing scheme based on a secure modular embedding, computed using quantized random projections, being a generalization of previous work in the area. Secure Modular Hashes (SMH) extracted from the signals preserve information about the distance between the signals, hiding other characteristic from the signals. Theoretical properties state that the described scheme provides a mechanism to threshold how much information to reveal, and is also information theoretically secure above this threshold. Finally, experimental results reveal that distances computed from SMH vectors can effectively replace the actual Euclidean distances with minimal degradation. We present a novel method to securely determine whether two signals are similar to each other, and apply it to approximate nearest neighbor clustering. The proposed method relies on a locality sensitive hashing scheme based on a secure binary embedding, computed using quantized random projections. Hashes extracted from the signals preserve information about the distance between the signals, provided this distance is small enough. If the distance between the signals is larger than a threshold, then no information about the distance is revealed. Theoretical and experimental justification is provided for this property. Further, when the randomized embedding parameters are unknown, then the mutual information between the hashes of any two signals decays to zero exponentially fast as a function of the l 2 distance between the signals. Taking advantage of this property, we suggest that these binary hashes can be used to perform privacy-preserving nearest neighbor search with significantly lower complexity compared to protocols which use the actual signals.