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
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A novel homogeneity-based method for music structure analysis is proposed. The heart of the method is a similarity measure, derived from first principles, that is based on the matrix Elastic Net (EN) regularization and deals efficiently with highly correlated audio feature vectors. In particular, beat-synchronous mel-frequency cepstral coefficients, chroma features, and auditory temporal modulations model the audio signal. The EN induced similarity measure is employed to construct an affinity matrix, yielding a novel subspace clustering method referred to as Elastic Net subspace clustering (ENSC). The performance of the ENSC in structure analysis is assessed by conducting extensive experiments on the Beatles dataset. The experimental findings demonstrate the descriptive power of the EN-based affinity matrix over the affinity matrices employed in subspace clustering methods, attaining the state-of-the-art performance reported for the Beatles dataset. The vector representations of fixed dimensionality for words (in text) offered by Word2Vec have been shown to be very useful in many application scenarios, in particular due to the semantic information they carry. This paper proposes a parallel version, the Audio Word2Vec. It offers the vector representations of fixed dimensionality for variable-length audio segments. These vector representations are shown to describe the sequential phonetic structures of the audio segments to a good degree, with very attractive real world applications such as query-by-example Spoken Term Detection (STD). In this STD application, the proposed approach significantly outperformed the conventional Dynamic Time Warping (DTW) based approaches at significantly lower computation requirements. We propose unsupervised learning of Audio Word2Vec from audio data without human annotation using Sequence-to-sequence Audoencoder (SA). SA consists of two RNNs equipped with Long Short-Term Memory (LSTM) units: the first RNN (encoder) maps the input audio sequence into a vector representation of fixed dimensionality, and the second RNN (decoder) maps the representation back to the input audio sequence. The two RNNs are jointly trained by minimizing the reconstruction error. Denoising Sequence-to-sequence Autoencoder (DSA) is furthered proposed offering more robust learning. Cross media retrieval engines have gained massive popularity with rapid development of the Internet. Users may perform queries in a corpus consisting of audio, video, and textual information. To make such systems practically possible for large mount of multimedia data, two critical issues must be carefully considered: (a) reduce the storage as much as possible; (b) model the relationship of the heterogeneous media data. Recently academic community have proved that encoding the data into compact binary codes can drastically reduce the storage and computational cost. However, it is still unclear how to integrate multiple information sources properly into the binary code encoding scheme. In this paper, we study the cross media indexing problem by learning the discriminative hashing functions to map the multi-view datum into a shared hamming space. Not only meaningful within-view similarity is required to be preserved, we also incorporate the between-view correlations into the encoding scheme, where we map the similar points close together and push apart the dissimilar ones. To this end, we propose a novel hashing algorithm called Iterative Multi-View Hashing (IMVH) by taking these information into account simultaneously. To solve this joint optimization problem efficiently, we further develop an iterative scheme to deal with it by using a more flexible quantization model. In particular, an optimal alignment is learned to maintain the between-view similarity in the encoding scheme. And the binary codes are obtained by directly solving a series of binary label assignment problems without continuous relaxation to avoid the unnecessary quantization loss. In this way, the proposed algorithm not only greatly improves the retrieval accuracy but also performs strong robustness. An extensive set of experiments clearly demonstrates the superior performance of the proposed method against the state-of-the-art techniques on both multimodal and unimodal retrieval tasks. Acoustic Event Classification (AEC) has become a significant task for machines to perceive the surrounding auditory scene. However, extracting effective representations that capture the underlying characteristics of the acoustic events is still challenging. Previous methods mainly focused on designing the audio features in a 'hand-crafted' manner. Interestingly, data-learnt features have been recently reported to show better performance. Up to now, these were only considered on the frame-level. In this paper, we propose an unsupervised learning framework to learn a vector representation of an audio sequence for AEC. This framework consists of a Recurrent Neural Network (RNN) encoder and a RNN decoder, which respectively transforms the variable-length audio sequence into a fixed-length vector and reconstructs the input sequence on the generated vector. After training the encoder-decoder, we feed the audio sequences to the encoder and then take the learnt vectors as the audio sequence representations. Compared with previous methods, the proposed method can not only deal with the problem of arbitrary-lengths of audio streams, but also learn the salient information of the sequence. Extensive evaluation on a large-size acoustic event database is performed, and the empirical results demonstrate that the learnt audio sequence representation yields a significant performance improvement by a large margin compared with other state-of-the-art hand-crafted sequence features for AEC. The literature on content-based music retrieval has largely finessed acoustic issues by using MIDI format music. This paper however considers content-based classification and retrieval of a typical (MPEG layer III) digital music archive. Two statistical techniques are investigated and appraised. Gaussian mixture modelling performs well with an accuracy of 92 on a music classification task. A tree-based vector quantization scheme offers marginally worse performance in a faster, scalable framework. Good results are also reported for music retrieval-by-similarity using the same techniques. Mel-frequency cepstral coefficients parameterize the audio well, though are slow to compute from the compressed domain. A new parameterization (MP3CEP), based on a partial decompression of MPEG layer III audio, is therefore proposed to facilitate music processing at user-interactive speeds. Overall, the techniques described provide useful tools in the management of a typical digital music library. Similarity-preserving hashing is a widely-used method for nearest neighbour search in large-scale image retrieval tasks. For most existing hashing methods, an image is first encoded as a vector of hand-engineering visual features, followed by another separate projection or quantization step that generates binary codes. However, such visual feature vectors may not be optimally compatible with the coding process, thus producing sub-optimal hashing codes. In this paper, we propose a deep architecture for supervised hashing, in which images are mapped into binary codes via carefully designed deep neural networks. The pipeline of the proposed deep architecture consists of three building blocks: 1) a sub-network with a stack of convolution layers to produce the effective intermediate image features; 2) a divide-and-encode module to divide the intermediate image features into multiple branches, each encoded into one hash bit; and 3) a triplet ranking loss designed to characterize that one image is more similar to the second image than to the third one. Extensive evaluations on several benchmark image datasets show that the proposed simultaneous feature learning and hash coding pipeline brings substantial improvements over other state-of-the-art supervised or unsupervised hashing methods. We investigate the problem of matching symbolic representations directly to audio based representations for applications that use data from both domains. One such application is score alignment, which aligns a sequence of frames based on features such as chroma vectors and distance functions such as Euclidean distance. Good representations are critical, yet current systems use ad hoc constructions such as the chromagram that have been shown to work quite well. We investigate ways to learn chromagram-like representations that optimize the classification of “matching” vs. “non-matching” frame pairs of audio and MIDI. New representations learned automatically from examples not only perform better than the chromagram representation but they also reveal interesting projection structures that differ distinctly from the traditional chromagram. Electronic Music Distribution (EMD) is in demand of robust, automatically extracted music descriptors. We introduce a timbral similarity measures for comparing music titles. This measure is based on a Gaussian model of cepstrum coefficients. We describe the timbre extractor and the corresponding timbral similarity relation. We describe experiments in assessing the quality of the similarity relation, and show that the measure is able to yield interesting similarity relations, in particular when used in conjunction with other similarity relations. We illustrate the use of the descriptor in several EMD applications developed in the context of the Cuidado European project. Conventional methods for finding audio in databases typically search text labels, rather than the audio itself. This can be problematic as labels may be missing, irrelevant to the audio content, or not known by users. Query by vocal imitation lets users query using vocal imitations instead. To do so, appropriate audio feature representations and effective similarity measures of imitations and original sounds must be developed. In this paper, we build upon our preliminary work to propose Siamese style convolutional neural networks to learn feature representations and similarity measures in a unified end-to-end training framework. Our Siamese architecture uses two convolutional neural networks to extract features, one from vocal imitations and the other from original sounds. The encoded features are then concatenated and fed into a fully connected network to estimate their similarity. We propose two versions of the system: IMINET is symmetric where the two encoders have an identical structure and are trained from scratch, while TL-IMINET is asymmetric and adopts the transfer learning idea by pretraining the two encoders from other relevant tasks: spoken language recognition for the imitation encoder and environmental sound classification for the original sound encoder. Experimental results show that both versions of the proposed system outperform a state-of-the-art system for sound search by vocal imitation, and the performance can be further improved when they are fused with the state of the art system. Results also show that transfer learning significantly improves the retrieval performance. This paper also provides insights to the proposed networks by visualizing and sonifying input patterns that maximize the activation of certain neurons in different layers. The problem of similarity search in large time series databases has attracted much attention recently. It is a non-trivial problem because of the inherent high dimensionality of the data. The most promising solutions involve first performing dimensionality reduction on the data, and then indexing the reduced data with a spatial access method. Three major dimensionality reduction techniques have been proposed: Singular Value Decomposition (SVD), the Discrete Fourier transform (DFT), and more recently the Discrete Wavelet Transform (DWT). In this work we introduce a new dimensionality reduction technique which we call Piecewise Aggregate Approximation (PAA). We theoretically and empirically compare it to the other techniques and demonstrate its superiority. In addition to being competitive with or faster than the other methods, our approach has numerous other advantages. It is simple to understand and to implement, it allows more flexible distance measures, including weighted Euclidean queries, and the index can be built in linear time. Cover Song Identification (CSI) technique, refers to the process of identifying an alternative version, performance, rendition, or recording of a previously recorded musical composition by measuring and modeling the musical similarity between them quantitatively and objectively. However, it is not possible to describe the similarity between tracks comprehensively and reliably with only one similarity function. In this paper, the Similarity Network Fusion (SNF) technique, which was originally proposed for combining different kernels for predicting drug-target interactions, is adopted to fuse different similarities based on the same descriptor and different similarity functions. First, the Harmonic Pitch Class Profile (HPCP) is extracted from each track. Next, the similarities, in terms of Qmax and Dmax measures, between the HPCP descriptors of any two tracks are calculated, respectively. Then, the track-by-track similarity networks based on Qmax and on Dmax similarity are constructed separately and then fused into one network by SNF. Finally, the fused similarities obtained from the fused similarity network are adopted to train a classifier, which can then be used to identify whether the input two tracks belong to reference cover or reference non-cover pair. Experimental results on Covers80 (http: labrosa.ee.columbia.edu projects coversongs covers80 ), subset of SecondHandSongs (SHS) (http: labrosa.ee.columbia.edu millionsong secondhand), and the Mixed Collection and Mazurka Cover Collection provided by MIREX (http: www.music-ir.org mirex wiki 2016:Audio_Cover_Song_Identification) demonstrate that the proposed scheme performs comparably with or even better than state-of-the-art CSI schemes. Cross-media hashing, which conducts cross-media retrieval by embedding data from different modalities into a common low-dimensional Hamming space, has attracted intensive attention in recent years. The existing cross-media hashing approaches only aim at learning hash functions to preserve the intra-modality and inter-modality correlations, but do not directly capture the underlying semantic information of the multi-modal data. We propose a discriminative coupled dictionary hashing (DCDH) method in this paper. In DCDH, the coupled dictionary for each modality is learned with side information (e.g., categories). As a result, the coupled dictionaries not only preserve the intra-similarity and inter-correlation among multi-modal data, but also contain dictionary atoms that are semantically discriminative (i.e., the data from the same category is reconstructed by the similar dictionary atoms). To perform fast cross-media retrieval, we learn hash functions which map data from the dictionary space to a low-dimensional Hamming space. Besides, we conjecture that a balanced representation is crucial in cross-media retrieval. We introduce multi-view features on the relatively weak'' modalities into DCDH and extend it to multi-view DCDH (MV-DCDH) in order to enhance their representation capability. The experiments on two real-world data sets show that our DCDH and MV-DCDH outperform the state-of-the-art methods significantly on cross-media retrieval. Non-negative Matrix Factorisation (NMF) based algorithms have found application in monaural audio source separation due to their ability to factorize audio spectrogram into additive part-based basis functions, which typically corresponds to individual notes or chords in music. These separated basis functions are usually greater in number than the active sources, hence clustering is needed for individual source signal synthesis. Although, many attempts have been made to improve the clustering of the basis functions to sources, much research is still required in this area. Recently, Shifted NMF based methods have been proposed as a means to avoid clustering these pitched basis functions to sources. However, the Shifted NMF algorithm uses a log-frequency spectrogram with a xed number of frequency bins per octave which compromises the quality of separated sources.We show that by replacing the method used to calculate the log-frequency spectrogram with a recently proposed invertible Constant Q Transform (CQT), we can considerably improve the separation quality of the individual sound signals. Recently, learning based hashing methods have become popular for indexing large-scale media data. Hashing methods map high-dimensional features to compact binary codes that are efficient to match and robust in preserving original similarity. However, most of the existing hashing methods treat videos as a simple aggregation of independent frames and index each video through combining the indexes of frames. The structure information of videos, e.g., discriminative local visual commonality and temporal consistency, is often neglected in the design of hash functions. In this paper, we propose a supervised method that explores the structure learning techniques to design efficient hash functions. The proposed video hashing method formulates a minimization problem over a structure-regularized empirical loss. In particular, the structure regularization exploits the common local visual patterns occurring in video frames that are associated with the same semantic class, and simultaneously preserves the temporal consistency over successive frames from the same video. We show that the minimization objective can be efficiently solved by an Accelerated Proximal Gradient (APG) method. Extensive experiments on two large video benchmark datasets (up to around 150K video clips with over 12 million frames) show that the proposed method significantly outperforms the state-of-the-art hashing methods. Efficient similarity retrieval from large-scale multimodal database is pervasive in current search systems with the big data tidal wave. To support queries across content modalities, the system should enable cross-modal correlation and computation-efficient indexing. While hashing methods have shown great potential in approaching this goal, current attempts generally failed to learn isomorphic hash codes in a seamless scheme, that is, they embed multiple modalities into a continuous isomorphic space and then threshold embeddings into binary codes, which incurred substantial loss of search quality. In this paper, we establish seamless multimodal hashing by proposing a novel Compositional Correlation Quantization (CCQ) model. Specifically, CCQ jointly finds correlation-maximal mappings that transform different modalities into an isomorphic latent space, and learns compositional quantizers that quantize the isomorphic latent features into compact binary codes. An optimization framework is developed to preserve both intra-modal similarity and inter-modal correlation while minimizing both reconstruction and quantization errors, which can be trained from both paired and unpaired data in linear time. A comprehensive set of experiments clearly show the superior effectiveness and efficiency of CCQ against the state-of-the-art techniques on both unimodal and cross-modal search tasks. We observed that for multimedia data – especially music collaborative similarity measures perform much better than similarity measures derived from content-based sound features. Our observation is based on a large scale evaluation with >250,000,000 collaborative data points crawled from the web and >190,000 songs annotated with content-based sound feature sets. A song mentioned in a playlist is regarded as one collaborative data point. In this paper we present a novel approach to bridging the performance gap between collaborative and contentbased similarity measures. In the initial training phase a model vector for each song is computed, based on collaborative data. Each vector consists of 200 overlapping unlabelled 'genres' or song clusters. Instead of using explicit numerical voting, we use implicit user profile data as collaborative data source, which is, for example, available as purchase histories in many large scale ecommerce applications. After the training phase, we used support vector machines based on content-based sound features to predict the collaborative model vectors. These predicted model vectors are finally used to compute the similarity between songs. We show that combining collaborative and content-based similarity measures can help to overcome the new item problem in e-commerce applications that offer a collaborative similarity recommender as service to their customers. The recent years have witnessed the emerging of vector quantization (VQ) techniques for efficient similarity search. VQ partitions the feature space into a set of codewords and encodes data points as integer indices using the codewords. Then the distance between data points can be efficiently approximated by simple memory lookup operations. By the compact quantization, the storage cost, and searching complexity are significantly reduced, thereby facilitating efficient large-scale similarity search. However, the performance of several celebrated VQ approaches degrades significantly when dealing with noisy data. In addition, it can barely facilitate a wide range of applications as the distortion measurement only limits to @math norm . To address the shortcomings of the squared Euclidean ( @math norm) loss function employed by the VQ approaches, in this paper, we propose a novel robust and general VQ framework, named RGVQ, to enhance both robustness and generalization of VQ approaches. Specifically, a @math -norm loss function is proposed to conduct the @math -norm similarity search, rather than the @math norm search, and the @math -th order loss is used to enhance the robustness. Despite the fact that changing the loss function to @math norm makes VQ approaches more robust and generic, it brings us a challenge that a non-smooth and non-convex orthogonality constrained @math - norm function has to be minimized. To solve this problem, we propose a novel and efficient optimization scheme and specify it to VQ approaches and theoretically prove its convergence. Extensive experiments on benchmark data sets demonstrate that the proposed RGVQ is better than the original VQ for several approaches, especially when searching similarity in noisy data. We propose a new blind segmentation approach to acoustic event detection (AED) based on i-vectors. Conventional approaches to AED often required well-segmented data with non-overlapping boundaries for competing events. Inspired by block-based automatic image annotation in image retrieval tasks, we blindly segment audio streams into equal-length pieces, label the underlying observed acoustic events with multiple categories and with no event boundary information, extract i-vector for them, and perform classification using support vector machine and maximal figure-of-merit based classifiers. Experiments on various sets of audio data show promising results with an average of 8 absolute gain in F1 over the conventional hidden Markov model based approach. An enhanced robustness at different noise levels is also observed. The key to the success lies in the enhanced discrimination power offered by the i-vector representation of the acoustic data. Index Terms: acoustic event detection, i-vector, blind segmentation, support vector machine, maximal figure-of-merit Binary code learning, a.k.a., hashing, has been recently popular due to its high efficiency in large-scale similarity search and recognition. It typically maps high-dimensional data points to binary codes, where data similarity can be efficiently computed via rapid Hamming distance. Most existing unsupervised hashing schemes pursue binary codes by reducing the quantization error from an original real-valued data space to a resulting Hamming space. On the other hand, most existing supervised hashing schemes constrain binary code learning to correlate with pairwise similarity labels. However, few methods consider ordinal relations in the binary code learning process, which serve as a very significant cue to learn the optimal binary codes for similarity search. In this paper, we propose a novel hashing scheme, dubbed Ordinal Embedding Hashing (OEH), which embeds given ordinal relations among data points to learn the ranking-preserving binary codes. The core idea is to construct a directed unweighted graph to capture the ordinal relations, and then train the hash functions using this ordinal graph to preserve the permutation relations in the Hamming space. To learn such hash functions effectively, we further relax the discrete constraints and design a stochastic gradient decent algorithm to obtain the optimal solution. Experimental results on two large-scale benchmark datasets demonstrate that the proposed OEH method can achieve superior performance over the state-of-the-arts approaches. At last, the evaluation on query by humming dataset demonstrates the OEH also has good performance for music retrieval by using user's humming or singing. Most existing cross-modal hashing methods suffer from the scalability issue in the training phase. In this paper, we propose a novel cross-modal hashing approach with a linear time complexity to the training data size, to enable scalable indexing for multimedia search across multiple modals. Taking both the intra-similarity in each modal and the inter-similarity across different modals into consideration, the proposed approach aims at effectively learning hash functions from large-scale training datasets. More specifically, for each modal, we first partition the training data into @math clusters and then represent each training data point with its distances to @math centroids of the clusters. Interestingly, such a k-dimensional data representation can reduce the time complexity of the training phase from traditional O(n2) or higher to O(n), where @math is the training data size, leading to practical learning on large-scale datasets. We further prove that this new representation preserves the intra-similarity in each modal. To preserve the inter-similarity among data points across different modals, we transform the derived data representations into a common binary subspace in which binary codes from all the modals are "consistent" and comparable. nThe transformation simultaneously outputs the hash functions for all modals, which are used to convert unseen data into binary codes. Given a query of one modal, it is first mapped into the binary codes using the modal's hash functions, followed by matching the database binary codes of any other modals. Experimental results on two benchmark datasets confirm the scalability and the effectiveness of the proposed approach in comparison with the state of the art.