Dynamic time warping

About: Dynamic time warping is a research topic. Over the lifetime, 6013 publications have been published within this topic receiving 133130 citations.

Mean field networks that learn to discriminate temporally distorted strings

Abstract: Neural networks can be used to discriminate between very similar phonemes and they can handle the variability in time of occurrence by using a time-delay architecture followed by a temporal integration (Lang, Hinton and Waibel, 1990) So far, however, neural networks have been less successful at handling longer duration events that require something equivalent to “time warping” in order to match stored knowledge to the data We present a type of mean field network (MFN) with tied weights that is capable of approximating the recognizer for a hidden markov model (HMM) In the process of settling to a stable state, the MFN finds a blend of likely ways of generating the input string given its internal model of the probabilities of transitions between hidden states and the probabilities of input symbols given a hidden state This blend is a heuristic approximation to the full set of path probabilities that is implicitly represented by an HMM recognizer The learning algorithm for the MFN is less efficient than for an HMM of the same size However, the MFN is capable of using distributed representations of the hidden state, and this can make it exponentially more efficient than an HMM when modelling strings produced by a generator that itself has componential states We view this type of MFN as a way of allowing more powerful representations without abandoning the automatic parameter estimation procedures that have allowed relatively simple models like HMM's to outperform complex AI representations on real tasks

kNN ensembles with penalized DTW for multivariate time series imputation

Abstract: The imputation of partially missing multivariate time series data is critical for its correct analysis. The biggest problems in time series data are consecutively missing values that would result in serious information loss if simply dropped from the dataset. To address this problem, we adapt the k-Nearest Neighbors algorithm in a novel way for multivariate time series imputation. The algorithm employs Dynamic Time Warping as distance metric instead of point-wise distance measurements. We preprocess the data with linear interpolation to create complete windows for Dynamic Time Warping. The algorithm derives global distance weights from the correlation between features and consecutively missing values are penalized by individual distance weights to reduce error transfer from linear interpolation. Finally, efficient ensemble methods improve the accuracy. Experimental results show accurate imputations on datasets with a high correlation between features. Further, our algorithm shows better results with consecutively missing values than state-of-the-art algorithms.

Automatic identification of bird calls using Spectral Ensemble Average Voice Prints

Abstract: Automatic identification of bird calls without manual intervention has been a challenging task for meaningful research on the taxonomy and monitoring of bird migrations in ornithology. In this paper we apply several techniques used in speech recognition to the automatic identification of bird calls. A new technique which computes the ensemble average on the FFT spectrum is proposed for identification of bird calls. This ensemble average is computed on the FFT spectrum of each bird and is called the Spectral Ensemble Average Voice Print (SEAV) of that particular bird. The SEAV of various birds are computed and are found to be different when compared to each other. A database of bird calls is created from the available recordings of fifteen bird species. The SEAV is then used for the identification of bird calls from this database. The results of identification using SEAV are then compared against the results derived from common classifiers used in speech recognition like dynamic time warping (DTW), Gaussian mixture modeling (GMM). A one level and two level classifier combination is also tried by combining SEAV classifier with the DTW classifier. The SEAV is computationally less expensive when compared to DTW or the GMM based classifiers while performing better than the DTW technique. Several new possibilities in automatic bird call identification using SEAV are also listed.

Segmental Duration and Speech Timing

Abstract: Many speech technologies assume that speech can be approximated by time warping and concatenating appropriately selected “speech units”, such as diphones. This paper first discusses evidence for the validity of this assumption, and then points out how modelling of speech timing can be cast in terms of time warp functions; conventional segmental duration based modelling is a special case of time warp function based modelling. Next, the paper addresses a challenge against time warp based modelling: the possible existence of long-range temporal constraints on timing, as proposed by isochrony and syllabic timing concepts. However, evidence is provided that such constraints simply do not exist in American English and Mandarin Chinese. The paper concludes with a presentation of a time warp based approach to pitch modelling.