List of Journal and Conference Papers, Posters and Abstracts, Technical Reports, Talks and Patents
NOTE: Check my CV for a detailed profile.
Show/Hide All- PhD Dissertation - Show/Hide
- Latent Variable Framework for Modeling and Separating Single Channel Acoustic Sources.
AbstractAuditory Scene Analysis refers to the human ability to extract different perceptual objects from a sound mixture. Replicating this ability in artificial systems has been an active area of research, related both to how one characterizes acoustic sources and separates sources from mixtures. The focus of this thesis is to develop models and algorithms that provide a framework to address these questions. The framework comprises latent variable models that employ hidden variables to model unobservable quantities. Such models are appropriate for obtaining representations of data that make hidden structure explicit. This work shows how one can utilize these ideas for the problem of source separation using single-channel audio signals.
The proposed framework focuses on learning the time-frequency (TF) structure in a data-driven manner. TF representations of sounds are modeled by treating the energy in every TF bin as histogram counts of multiple draws. This formulation allows the extraction of the characteristic frequency structure of individual sources as latent components and models the sources as additive combinations of these components. The framework is then extended to incorporate the idea of sparse coding to overcome an important limitation of the basic model: an upper bound on the number of extractable components. Sparsity, imposed in the form of an entropic prior distribution, allows extraction of overcomplete sets of components that are more expressive and better characterize the sources. The statistical foundation of the framework makes it amenable to other extensions where known or hypothesized structure about the data can be easily incorporated by imposing appropriate prior distributions. Theoretical analysis of the proposed methods and algorithms for parameter inference are presented.
Applications of the models to real-world problems are evaluated and discussed. The latent components learned from acoustic sources are used in a supervised setting for source separation and in a semi-supervised setting for denoising. Unlike approaches based on time-frequency masks that reconstruct partial spectral descriptions of sources by identifying time-frequency bins in which a source dominates, this approach reconstructs entire spectral descriptions of all sources. Various experimental results demonstrate the utility of the proposed framework.
- Latent Variable Framework for Modeling and Separating Single Channel Acoustic Sources.
- Journal Papers - Show/Hide
- Missing Data Imputation for Time-Frequency Representations of Audio Signals.
AbstractWith the recent attention towards audio processing in the time-frequency domain we increasingly encounter the problem of missing data within that representation. In this paper we present an approach that allows us to recover missing values in the time-frequency domain of audio signals. The presented approach is able to deal with real-world polyphonic signals by operating seamlessly even in the presence of complex acoustic mixtures. We demonstrate that this approach outperforms generic missing data approaches, and we present a variety of situations that highlight its utility. - Probabilistic Latent Variable Models as Non-Negative Factorizations.
AbstractThis paper presents a family of probabilistic latent variable models that can be used for analysis of nonnegative data. We show that there are strong ties between nonnegative matrix factorization and this family, and provide some straightforward extensions which can help in dealing with shift invariances, higher-order decompositions and sparsity constraints. We argue through these extensions that the use of this approach allows for rapid development of complex statisticalmodels for analyzing nonnegative data. - A Framework for Secure Speech Recognition.
AbstractIn this paper, we present a process which enables privacy-preserving speech recognition transactions between two parties. We assume one party with private speech data and one party with private speech recognition models. Our goal is to enable these parties to perform a speech recognition task using their data, but without exposing their private information to each other. We will demonstrate how using secure multiparty computation principles we can construct a system where this transaction is possible, and how this system is computationally and securely correct. The protocols described herein can be used to construct a rudimentary speech recognition system and can easily be extended for arbitrary audio and speech processing. - Optimal Skewed Data
Allocation on Multiple Channels with Flat Broadcast per Channel.*
AbstractBroadcast is an efficient and scalable way of transmitting data to an unlimited number of clients that are listening to a channel. Cyclically broadcasting data over the channel is a basic scheduling technique, which is known as flat scheduling. When multiple channels are available, a data allocation technique is needed to assign data to channels. Partitioning data among channels in an unbalanced way, depending on data popularities, is an allocation technique known as skewed allocation. The problem of data broadcasting over multiple channels is considered, assuming skewed data allocation to channels and flat data scheduling per channel, with the objective of minimizing the average waiting time of the clients. First, several algorithms, based on dynamic programming, are presented which provide optimal solutions for N data items and K channels. Specifically, for data items with uniform lengths, an O(NK log N) time algorithm is proposed, which improves over the previously known O(N/sup 2/K) time algorithm. When K/spl les/4, a simpler O(N log N) time algorithm is exhibited which requires only O(N) time if the data items are sorted. Moreover, for data items with nonuniform lengths, it is shown that the problem is NP-hard when K=2 and strong NP-hard for arbitrary K. In the former case, a pseudopolynomial algorithm is discussed whose time is O(NZ), where Z is the sum of the data lengths. In the latter case, an algorithm is devised with time exponential in the maximum data length, which can optimally solve, in reasonable time, only small instances. For larger instances, a new heuristic is devised which is experimentally tested on some benchmarks whose popularities are characterized by Zipf distributions. Such experimental tests reveal that the new heuristic proposed here always outperforms the best previously known heuristic in terms of solution quality. - A Characterisation of Optimal Channel Assignments for
Cellular and Square Grid Wireless Networks.
AbstractIn this paper we first present a uniformity property that characterises optimal channel assignments for networks arranged as cellular or square grids. Then, we present optimal channel assignments for cellular and square grids; these assignments exhibit a high value for delta_1 - the separation between channels assigned to adjacent stations. We prove an upper bound on delta_1 for such optimal channel assignments. This upper bound is greater than the value of delta_1 exhibited by our assignments. Based on empirical evidence, we conjecture that the value our assignments exhibit is a tight upper bound on delta_1.
- Missing Data Imputation for Time-Frequency Representations of Audio Signals.
- Conference Proceedings - Show/Hide
- Simplex Decompositions using SVD and PLSA.
AbstractProbabilistic Latent Semantic Analysis (PLSA) is a popular technique to analyze non-negative data where multinomial distributions underlying every data vector are expressed as linear combinations of a set of basis distributions. These learned basis distributions that characterize the dataset lie on the standard simplex and themselves represent corners of a simplex within which all data approximations lie. In this paper, we describe a novel method to extend the PLSA decomposition where the bases are not constrained to lie on hte standard simplex and thus are better able to characterize the data. The locations of PLSA basis distributions on the standard simplex depend on how the dataset is aligned with respect to the standard simplex. If the directions of maximum variance of the dataset are orthogonal to the standard simplex, then the PLSA bases will give a poor representation of the dataset. Out approach overcomes this drawback by utilizing Singular Values Decomposition (SVD) to identify the directions of maximum variane, and transforming the dataset to align these directions paralle to the standard simplex before performing PLSA. These learned PLSA features are then transformed back into the data space. The effectiveness of the proposed approach is demonstrated with experiments on synthetic data. - Copula Functions for Learning Multimodal Densities with Non-linear Dependencies.
- A Fast Algorithm for Discrete HMM Training using Observed Transitions.
AbstractWe present a new algorithm to estimate the parameters of a Hidden Markov Model (HMM), specifically the transition probability matrix of the hidden states and the emission probabilities, given an observed sequence of data. The algorithm uses the number of transitions present in the observed label sequence and computes parameters in an iterative fashion. We present experiments that demonstrate significant speed gains obtained by the current algorithm as compared to traditional algorithms such as Baum-Welch iterations. - A Privacy Preserving Framework for Gaussian Mixture Models.
AbstractThis paper presents a framework for privacy-preserving Gaussian Mixture Model computations. Specifically, we consider a scenario where a central service wants to learn the parameters of a Gaussian Mixture Model from private data distributed among multiple parties with privacy constraints. In addition, the service also has security constraints where none of the data owners are allowed to learn the values of the trained parameters. We use Secure Multiparty Computations to propose a framework that allows such computations.In addition, we also show how such a central service can classify new test data from privacy constrained third parties without exposing the learned models. The classification occurs with the added constraint that the service learns no information about either the test data or the result of the classification. - Probabilistic Latent Component Analysis for Gearbox Vibration Source Separation.
AbstractProbabilistic Latent Component Analysis (PLCA) is applied to the problem of gearbox vibration source separation. A model for the probability distribution of gearbox vibration employs a latent variable intended to correspond to a particular vibration source, with the measured vibration at a particular sensor for each source the product of a marginal distribution of vibration by frequency, a marginal distribution of vibration by shaft rotation, and a sensor weight distribution. An expectation-maximization algorithm is used to approximate a maximum-likelihood parametrization for the model. In contrast to other unsupervised source separation methods, PLCA allows for separation of vibration sources when there are fewer vibration sensors than vibration sources. Once the vibration components of a healthy gearbox have been identified, the vibration characteristics of damaged gearbox elements can be determined. The efficacy of the technique is demonstrated with an application on a gearbox vibration data set. - Topic Models for Audio Mixture Analysis.
- A Sparse Non-parametric Approach for Single Channel Separation of Known Sounds.
AbstractIn this paper we present an algorithm for separating mixed sounds from a monophonic recording. Our approach makes use of training data which allows us to learn representations of the types of sounds that compose the mixture. In contrast to popular methods that attempt to extract com- pact generalizable models for each sound from training data, we employ the training data itself as a representation of the sources in the mixture. We show that mixtures of known sounds can be described as sparse com- binations of the training data itself, and in doing so produce significantly better separation results as compared to similar systems based on compact statistical models. - Simplex Decompositions for Real-valued Datasets.
AbstractIn this paper, we introduce the concept of Simplex Decompositions and present a new semi-nonnegative decomposition technique that works with real-valued datasets. The motivation stems from the limitations of topic models such as Probabilistic Latent Semantic Analysis (PLSA), that have found wide use in the analysis of non-negative data apart from text corpora such as images, audio spectra, gene array data among others. The goal of this paper is to remove the non-negativity requirement for datasets so that these models can work on datasets with both positive and negative entries. We start by showing that PLSA is equivalent to finding a set of components that define the corners of a simplex within which all datapoints lie. We formalize this intuition by introducing the notion of simplex decompositions - PLSA and extensions are specific examples - and generalize the idea to be applicable to arbitrary real datasets with both positive and negative entries. We present algorithms and illustrate the method with examples. - Missing Data Imputation for Spectral Audio Signals.
AbstractWith the recent attention to audio processing in the time-frequency domain, we increasingly encounter the problem of missing data. In this paper, we present an approach that allows for imputing missing values in the time-frequency domain of audio signals. The presented approach is able to deal with real-world polyphonic signals by performing imputation even in the presence of complex mixtures. We show that this approach outperforms generic imputation approaches, and we present a variety of situations that highlight its utility. - Mining Retail Transaction Data for Targeting Customers with Headroom - A Case Study.
AbstractWe outline a method to model customer behavior from retail transaction data. In particular, we focus on the problem of recommending relevant products to consumers. Addressing this problem of filling holes in the baskets of consumers is a fundamental aspect for the success of targeted promotion programs. Another important aspect is the identification of customers who are most likely to spend significantly and whose potential spending ability is not being fully realized. We discuss how to identify such customers with headroom and describe how relevant product categories can be recommended. The data consisted of individual transactions collected over a span of 16 months from a leading retail chain. The method is based on Singular Value Decomposition and can generate significant value for retailers. - Probabilistic Factorization of Non-Negative Data with Entropic Co-occurrence Constraints.
AbstractIn this paper we present a probabilistic algorithm which factorizes non-negative data. We employ entropic priors to additionally satisfy that user specified pairs of factors in this model will have their cross entropy maximized or minimized. These priors allow us to construct factorization algorithms that result in maximally statistically different factors, something that generic non-negative factorization algorithms cannot explicitly guarantee. We further show how this approach can be used to discover clusters of factors which allow a richer description of data while still effectively performing a low rank analysis. - Sparse and Shift-Invariant Feature Extraction from Non-Negative Data.
AbstractIn this paper we describe a technique that allows the extraction of multiple local shift-invariant features from analysis of non-negative data of arbitrary dimensionality. Our approach employs a probabilistic latent variable model with sparsity constraints. We demonstrate its utility by performing feature extraction in a variety of domains ranging from audio to images and video. - Sparse Overcomplete Latent Variable Decomposition of Counts Data. [ Fig.1 Data ]
AbstractAn important problem in many fields is the analysis of counts data to extract meaningful latent components. Methods like Probabilistic Latent Semantic Analysis (PLSA) and Latent Dirichlet Allocation (LDA) have been proposed for this purpose. However, they are limited in the number of components they can extract and lack an explicit provision to control the expressiveness of the extracted components. In this paper, we present a learning formulation to address these limitations by employing the notion of sparsity. We start with the PLSA framework and use an entropic prior in a maximum a posteriori formulation to enforce sparsity.We show that this allows the extraction of overcomplete sets of latent components which better characterize the data. We present experimental evidence of the utility of such representations. - Privacy-Preserving Musical Database Matching.
AbstractIn this paper we present an illustratory process which allows privacy-preserving transactions in the context of musical databases. In particular we address the problem of matching a piece of music audio to a service database in such a way such that the database provider will not directly observe the query, nor its result, thereby preserving the privacy of the inquirer. We formulate this process within the field of secure multiparty computation and show how such a transaction can be achieved once we derive secure versions of basic signal processing operations. - Supervised and Semi-Supervised Separation of Sounds from Single-Channel Mixtures.
AbstractIn this paper we describe a methodology for model-based single channel separation of sounds. We present a sparse latent variable model that can learn sounds based on their distribution of time/frequency energy. This model can then be used to extract known types of sounds from mixtures in two scenarios. One being the case where all sound types in the mixture are known, and the other being being the case where only the target or the interference models are known. The model we propose has close ties to non-negative decompositions and latent variable models commonly used for semantic analysis. - Sparse Overcomplete Decomposition for Single Channel Speaker Separation. [ Examples ]
AbstractWe present an algorithm for separating multiple speakers from a mixed single channel recording. The algorithm is based on a model proposed by Raj and Smaragdis (2005). The idea is to extract certain characteristic spectra-temporal basis functions from training data for individual speakers and decompose the mixed signals as linear combinations of these learned bases. In other words, their model extracts a compact code of basis functions that can explain the space spanned by spectral vectors of a speaker. In our model, we generate a sparse-distributed code where we have more basis functions than the dimensionality of the space. We propose a probabilistic framework to achieve sparsity. Experiments show that the resulting sparse code better captures the structure in data and hence leads to better separation. - A Framework for Secure Speech Recognition.
AbstractWe present an algorithm that enables privacy-preserving speech recognition transactions between multiple parties. We assume two commonplace scenarios. One being the case where one of two parties has private speech data to be transcribed and the other party has private models for speech recognition. And the other being that of one party having a speech model to be trained using private data of multiple other parties. In both of the above cases data privacy is desired from both the data and the model owners. In this paper we will show how such collaborations can be performed while ensuring no private data leaks using secure multiparty computations. In neither case will any party obtain information on other parties data. The protocols described herein can be used to construct rudimentary speech recognition systems and can be easily extended for arbitrary audio and speech processing. - Bandwidth Expansion with a Polya Urn Model. [ Examples ]
AbstractWe present a new statistical technique for the estimation of the high frequency components (4-8 kHz) of speech signals from narrow-band (0-4 kHz) signals. The magnitude spectra of broadband speech are modelled as the outcome of a Polya Urn process, that represents the spectra as the histogram of the outcome of several draws from a mixture multinomial distribution over frequency indices. The multinomial distributions that compose this process are learnt from a corpus of broadband (0-8 kHz) speech. To estimate high-frequency components of narrow-band speech, its spectra are also modelled as the outcome of draws from a mixture-multinomial process that is composed of the learnt multinomials, where the counts of the indices of higher frequencies have been obscured. The obscured high-frequency components are then estimated as the expected number of draws of their indices from the mixture-multinomial. Experiments conducted on bandlimited signals derived from the WSJ corpus show that the proposed procedure is able to accurately estimate the high frequency components of these signals. - Separating a Foreground Singer from Background Music. [ Examples ]
AbstractIn this paper we present a algorithm for separating singing voices from background music in popular songs. The algorithm is derived by modelling the magnitude spectrogram of audio signals as the outcome of draws from a discrete bi-variate random process that generates time-frequency pairs. The spectrogram of a song is assumed to have been obtained through draws from the distributions underlying the music and the vocals, respectively. The parameters of the underlying distribuiton are learnt from the observed spectrogram of the song. The spectrogram of the separated vocals is then derived by estimating the fraction of draws that were obtained from its distribution. In the paper we present the algorithm within a framework that allows personalization of popular songs, by separating out the vocals, processing them appropriately to one’s own tastes, and remixing them. Our experiments reveal that we are effectively able to separate out the vocals in a song and personalize them to our tastes. - A Probabilistic Latent Variable Model for Acoustic Modeling.
AbstractIn this paper we describe a model developed for the analysis of acoustic spectra. Unlike decompositions techniques that can result in difficult to interpret results this model explicitly models spectra as distributions and extracts sets of additive and semantically useful components that facilitate a variety of applications ranging from source separation, denoising, music transcription and sound recognition. This model is probabilistic in nature and is easily extended to produce sparse codes, and discover transform invariant components which can be optimized for particular applications. - Secure Sound Classification: Gaussian Mixture Models.*
AbstractWe propose secure protocols for Gaussian mixture-based sound recognition. The protocols we describe allow varying levels of security between two collaborating parties. The case we examine consists of one party (Alice) providing data and other party (Bob) providing a recognition algorithm. We show that it is possible to have Bob apply his algorithm on Alice's data in such a way that the data and the recognition results will not be revealed to Bob thereby guaranteeing Alice's data privacy. Likewise we show that it is possible to organize the collaboration so that a reverse engineering of Bob's recognition algorithm cannot be performed by Alice. We show how Gaussian mixtures can be implemented in a secure manner using secure computation primitives implementing simple numerical operations and we demonstrate the process by showing how it can yield identical results to a non-secure computation while maintaining privacy. - Latent Dirichlet Decomposition for Single Channel Speaker Separation. [ Examples ]
AbstractWe present an algorithm for the separation of multiple speakers from mixed single-channel recordings by latent variable decomposition of the speech spectrogram. We model each magnitude spectral vector in the short-time Fourier transform of a speech signal as the outcome of a discrete random process that generates frequency bin indices. The distribution of the process is modeled as a mixture of multinomial distributions, such that the mixture weights of the component multinomials vary from analysis window to analysis window. The component multinomials are assumed to be speaker specific and are learned from training signals for each speaker. We model the prior distribution of the mixture weights for each speaker as a Dirichlet distribution. The distributions representing magnitude spectral vectors for the mixed signal are decomposed into mixtures of the multinomials for all component speakers. The frequency distribution, i.e the spectrum for each speaker, is reconstructed from this decomposition. - Optimal Multi-Channel Data Allocation
with Flat Broadcast per Channel.*
AbstractBroadcast is an efficient and scalable way of transmitting data to an unlimited number of clients that are listening to a channel. Cyclically broadcasting data over the channel is a basic scheduling technique, which is known as flat scheduling. When multiple channels are available, partitioning data among channels in an unbalanced way, depending on data popularities, is an allocation technique known as skewed allocation. In this paper, the problem of data broadcasting over multiple channels is considered assuming skewed data allocation to channels and fiat data scheduling per channel, with the objective of minimizing the average waiting time of the clients. Several algorithms, based on dynamic programming, are presented which provide optimal solutions for N data items and K channels. Specifically, for data items with uniform lengths, an O(NKlogN) time algorithm is proposed, which improves over the previously known O(N/sup 2/K) time algorithm. When K /spl les/ 4, faster O(N) time algorithms are exhibited. Moreover, for data items with nonuniform lengths, it is shown that the problem is NP-hard when K = 2, and strong NP-hard for arbitrary K. In the former case, a pseudo-polynomial algorithm is discussed, whose time is O(NZ) where Z is the sum of the data lengths. - A Characterisation of Optimal Channel Assignments for Wireless
Networks Modelled as Cellular and Square Grids.
AbstractIn this paper we first present a uniformity property that characterises optimal channel assignments for networks arranged as cellular or square grids. Then, we present optimal channel assignments for cellular and square grids; these assignments exhibit a high value for /spl delta//sub 1/ - the separation between channels assigned to adjacent stations. Based on empirical evidence, we conjecture that the value our assignments exhibit is an upper bound on /spl delta//sub 1/.
- Channel Assignment for Wireless
Networks Modelled as d-Dimensional Square Grids.
AbstractIn this paper, we study the problem of channel assignment for wireless networks modelled as d-dimensional grids. In particular, for d-dimensional square grids, we present optimal assignments that achieve a channel separation of 2 for adjacent stations where the reuse distance is 3 or 4. We also introduce the notion of a colouring schema for d-dimensional square grids, and present an algorithm that assigns colours to the vertices of the grid satisfying the schema constraints.
- Simplex Decompositions using SVD and PLSA.
- Posters / Abstracts - Show/Hide
- Mining Retail Data for Targeting Customers with Headroom*.
- Separating and Understanding a Talker from a Mixture in Reverberant Spaces.
- The Role of Fundamental Frequency in Segregating and Understanding a Talker Competing with Another Talker in a Reverberant Setting.
- Effects of Reverberant Energy on
Statistics of Speech.
- Mining Retail Data for Targeting Customers with Headroom*.
- Technical Reports / Manuscripts - Show/Hide
- Probabilistic Latent Variable Model for Sparse Decompositions of Non-negative Data.
- Optimal Multi-Channel Data Allocation
with Flat Broadcast per Channel.*
- Probabilistic Latent Variable Model for Sparse Decompositions of Non-negative Data.
- Talks / Presentations - Show/Hide
- Probabilistic Latent Variable Models.
- Simplex Decompositions - Techniques for Nonnegative Data Analysis and Extensions.
- Simplex Decompositions.
- Latent Variable Framework for Modeling and Separating Single-Channel Acoustic Sources.
- Latent Variable Decomposition - Models and Applications (with Dr. Bhiksha Raj).
- Probabilistic Latent Semantic Analysis.
- Probabilistic Models for Single-Channel Audio Processing.
- Probabilistic Models for Single-Channel Audio Processing.
- Probabilistic Models for Acoustic Processing.
- Probabilistic Latent Variable Models.
- Patents - Show/Hide
- Method and System for Matching Audio Recordings.
- System and Method for Recognizing Speech Securely.
- Secure Classification of Data with Gaussian Distributions.
- Method and System for Matching Audio Recordings.
