There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality.

Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

I and i

Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

Digital processing of speech signals

From the Publisher:
Connectionist Speech Recognition: A Hybrid Approach describes the theory and implementation of a method to incorporate neural network approaches into state-of-the-art continuous speech recognition systems based on Hidden Markov Models (HMMs) to improve their performance. In this framework, neural networks (and in particular, multilayer perceptrons or MLPs) have been restricted to well-defined subtasks of the whole system, i.e., HMM emission probability estimation and feature extraction. The book describes a successful five year international collaboration between the authors. The lessons learned form a case study that demonstrates how hybrid systems can be developed to combine neural networks with more traditional statistical approaches. The book illustrates both the advantages and limitations of neural networks in the framework of a statistical system. Using standard databases and comparing with some conventional approaches, it is shown that MLP probability estimation can improve recognition performance. Other approaches are discussed, though there is no such unequivocal experimental result for these methods. Connectionist Speech Recognition: A Hybrid Approach is of use to anyone intending to use neural networks for speech recognition or within the framework provided by an existing successful statistical approach. This includes research and development groups working in the field of speech recognition, both with standard and neural network approaches, as well as other pattern recognition and/or neural network researchers. This book is also suitable as a text for advanced courses on neural networks or speech processing.

Connectionist Speech Recognition: A Hybrid Approach

This paper gives a general overview of techniques in statistical parametric speech synthesis. One of the instances of these techniques, called HMM-based generation synthesis (or simply HMM-based synthesis), has recently been shown to be very effective in generating acceptable speech synthesis. This paper also contrasts these techniques with the more conventional unit selection technology that has dominated speech synthesis over the last ten years. Advantages and disadvantages of statistical parametric synthesis are highlighted as well as identifying where we expect the key developments to appear in the immediate future.

https://www.sp.nitech.ac.jp/~bonanza/Paper/EMIME/zen_specom.pdf

Statistical Parametric Speech Synthesis

A new class of Support Vector Machine (SVM) that is applicable to sequential-pattern recognition such as speech recognition is developed by incorporating an idea of non-linear time alignment into the kernel function. Since the time-alignment operation of sequential pattern is embedded in the new kernel function, standard SVM training and classification algorithms can be employed without further modifications. The proposed SVM (DTAK-SVM) is evaluated in speaker-dependent speech recognition experiments of hand-segmented phoneme recognition. Preliminary experimental results show comparable recognition performance with hidden Markov models (HMMs).

/pdf/dynamic-time-alignment-kernel-in-support-vector-machine-5avzhgwcvo.pdf

Dynamic Time-Alignment Kernel in Support Vector Machine

This paper proposes a multipitch analyzer called the harmonic temporal structured clustering (HTC) method, that jointly estimates pitch, intensity, onset, duration, etc., of each underlying source in a multipitch audio signal. HTC decomposes the energy patterns diffused in time-frequency space, i.e., the power spectrum time series, into distinct clusters such that each has originated from a single source. The problem is equivalent to approximating the observed power spectrum time series by superimposed HTC source models, whose parameters are associated with the acoustic features that we wish to extract. The update equations of the HTC are explicitly derived by formulating the HTC source model with a Gaussian kernel representation. We verified through experiments the potential of the HTC method

/pdf/a-multipitch-analyzer-based-on-harmonic-temporal-structured-2ayzf0jy7h.pdf

A Multipitch Analyzer Based on Harmonic Temporal Structured Clustering

This paper presents a new sparse representation for acoustic signals which is based on a mixing model defined in the complex-spectrum domain (where additivity holds), and allows us to extract recurrent patterns of magnitude spectra that underlie observed complex spectra and the phase estimates of constituent signals. An efficient iterative algorithm is derived, which reduces to the multiplicative update algorithm for non-negative matrix factorization developed by Lee under a particular condition.

/pdf/complex-nmf-a-new-sparse-representation-for-acoustic-signals-2hoigc9kw7.pdf

Complex NMF: A new sparse representation for acoustic signals

The authors propose an algorithm, successive state splitting (SSS), for simultaneously finding an optimal set of phoneme context classes, an optimal topology, and optimal parameters for hidden Markov models (HMMs) commonly using a maximum likelihood criterion. With this algorithm, a hidden Markov network (HM-Net), which is an efficient representation of phoneme-context-dependent HMMs, can be generated automatically. The authors implemented this algorithm, and tested it on the recognition of six Japanese consonants ( mod b mod , mod d mod , mod g mod , mod m mod , mod n mod and mod N mod ). The HM-Net gave better recognition results with a lower number of total output probability density distributions than conventional phoneme-context-independent mixture Gaussian density HMMs. >

A successive state splitting algorithm for efficient allophone modeling

In this paper, we present a simple and fast method to separate a monaural audio signal into harmonic and percussive components, which is much useful for multi-pitch analysis, automatic music transcription, drum detection, modification of music, and so on. Exploiting the differences in the spectrograms of harmonic and percussive components, the objective function is defined in a quadrature form of the spectrogram gradients. Applying the auxiliary function approach to that, simple and fast update equations are derived, which guarantee the decrease of the objective function at each iteration. We show some experimental results by applying our method to popular and jazz music songs.

/pdf/separation-of-a-monaural-audio-signal-into-harmonic-3q1co1ssy8.pdf

Shigeki Sagayama

Papers

Dynamic Time-Alignment Kernel in Support Vector Machine

A Multipitch Analyzer Based on Harmonic Temporal Structured Clustering

Complex NMF: A new sparse representation for acoustic signals

A successive state splitting algorithm for efficient allophone modeling

Separation of a monaural audio signal into harmonic/percussive components by complementary diffusion on spectrogram