text: This paper presents a new method for a quick similarity-based search through long unlabeled audio streams to detect and locate audio clips provided by users. The method involves feature-dimension reduction based on a piecewise linear representation of a sequential feature trajectory extracted from a long audio stream. Two techniques enable us to obtain a piecewise linear representation: the dynamic segmentation of feature trajectories and the segment-based Karhunen-Loeve (KL) transform. The proposed search method guarantees the same search results as the search method without the proposed feature-dimension reduction method in principle. Experimental results indicate significant improvements in search speed. For example, the proposed method reduced the total search time to approximately 1 12 that of previous methods and detected queries in approximately 0.3 s from a 200-h audio database.
  @cite_4: List of Figures. Foreword M. Vetterli. Preface. 1. Signal Models and Analysis-Synthesis. 2. Sinusoidal Modeling. 3. Multiresolution Sinusoidal Modeling. 4. Residual Modeling. 5. Pitch-Synchronous Models. 6. Matching Pursuit and Atomic Models. 7. Conclusions. Appendix A: Two-Channel Filter Banks. Appendix B: Fourier Series Representations. References. References for Poetry Excerpts. Index.
  @cite_33: A low-bit-rate linear predictive coder (LPC) that is based on variable-length segment quantization is presented. In this vocoder, the speech spectral-parameter sequence is represented as the concatenation of variable-length spectral segments generated by linearly time-warping fixed-length code segments. Both the sequence of code segments and the segment lengths are efficiently determined using a dynamic programming procedure. This procedure minimizes the spectral distance measured between the original and the coded spectral sequence in a given interval. An iterative algorithm is developed for designing fixed-length code segments for the training spectral sequence. It updates the segment boundaries of the training spectral sequence using an a priori codebook and updates the codebook using these segment sequences. The convergence of this algorithm is discussed theoretically and experimentally. In experiments, the performance of variable-length segment quantization for voice coding is compared to that of fixed-length segment quantization and vector quantization. >
  @cite_21: The sinusoidal model has proven useful for representation and modification of speech and audio. One drawback, however, is that a sinusoidal signal model is typically derived using a fixed frame size, which corresponds to a rigid signal segmentation. For nonstationary signals, the resolution limitations that result from this rigidity lead to reconstruction artifacts. It is shown in this paper that such artifacts can be significantly reduced by using a signal-adaptive segmentation derived by a dynamic program. An atomic interpretation of the sinusoidal model is given; this perspective suggests that algorithms for adaptive segmentation can be viewed as methods for adapting the time scales of the constituent atoms so as to improve the model by employing appropriate time-frequency tradeoffs.
  @cite_1: 
  @cite_6: In this paper, we propose an efficient sinusoidal model of polyphonic audio signals especially good for the application of timescale modification. One of the critical problem of sinusoidal modeling is that the signal is smeared during the synthesis frame, which is a very undesirable effect for transient parts. We solve this problem by introducing multiresolution analysis-synthesis and dynamic segmentation methods. A signal is modeled with a sinusoidal component and a noise component. A multiresolution filter bank is applied to an input signal which splits it into octave-spaced subbands without causing aliasing and then sinusoidal analysis is applied to each subband signal. To alleviate smearing of transients during synthesis, a dynamic segmentation method is applied to the subband signals that determines the optimal analysis-synthesis frame size adaptively to fit its time-frequency characteristics. To extract sinusoidal components and calculate respective parameters, a matching pursuit algorithm is applied to each analysis frame of the subband signal. A psychoacoustic model implementing frequency masking is incorporated with matching pursuit to provide a reasonable stop condition of iteration and reduce the number of sinusoids. The noise component obtained by subtracting the synthesized signal with sinusoidal components from the original signal is modeled by a line-segment model of short time spectrum envelope. For various polyphonic audio signals, the results of simulation shows the proposed sinusoidal modeling can synthesize original signals without loss of perceptual quality and do more robust and high-quality timescale modification for large scale factors.
  @cite_24: For large vocabulary and continuous speech recognition, the sub-word-unit-based approach is a viable alternative to the whole-word-unit-based approach. For preparing a large inventory of subword units, an automatic segmentation is preferrable to manual segmentation as it substantially reduces the work associated with the generation of templates and gives more consistent results. In this paper we discuss some methods for automatically segmenting speech into phonetic units. Three different approaches are described, one based on template matching, one based on detecting the spectral changes that occur at the boundaries between phonetic units and one based on a constrained-clustering vector quantization approach. An evaluation of the performance of the automatic segmentation methods is given.
  @cite_25: We consider the segmentation of a trajectory into piecewise polynomial parts, or possibly other forms. Segmentation is typically formulated as an optimization problem which trades off model fitting error versus the cost of introducing new segments. Heuristics such as split-and-merge are used to find the best segmentation. We show that for ordered data (e.g., single curves or trajectories) the global optimum segmentation can be found by dynamic programming. The approach is easily extended to handle different segment types and top down information about segment boundaries, when available. We show segmentation results for video sequences of a basketball undergoing gravitational and nongravitational motion.
summary: Dynamic segmentation is a generic term that refers to techniques for dividing sequences into segments of various lengths. Dynamic segmentation methods for time-series signals have already been applied to various kinds of applications such as speech coding (e.g. @cite_33 ), the temporal compression of waveform signals @cite_1 , the automatic segmentation of speech signals into phonic units @cite_24 , sinusoidal modeling of audio signals @cite_4 @cite_21 @cite_6 and motion segmentation in video signals @cite_25 . We employ dynamic segmentation to minimize the average dimensionality of high-dimensional feature trajectories.