6 research outputs found

    Robust feature representation for classification of bird song syllables

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    A novel feature set for low-dimensional signal representation, designed for classification or clustering of non-stationary signals with complex variation in time and frequency, is presented. The feature representation of a signal is given by the first left and right singular vectors of its ambiguity spectrum matrix. If the ambiguity matrix is of low rank, most signal information in time direction is captured by the first right singular vector while the signal’s key frequency information is encoded by the first left singular vector. The resemblance of two signals is investigated by means of a suitable similarity assessment of the signals’ respective singular vector pair. Application of multitapers for the calculation of the ambiguity spectrum gives an increased robustness to jitter and background noise and a consequent improvement in performance, as compared to estimation based on the ordinary single Hanning window spectrogram. The suggested feature-based signal compression is applied to a syllable-based analysis of a song from the bird species Great Reed Warbler and evaluated by comparison to manual auditive and/or visual signal classification. The results show that the proposed approach outperforms well-known approaches based on mel-frequency cepstral coefficients and spectrogram cross-correlation

    What bird is singing?

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    The goal of this work was to create a model for characterizing bird species through recordings of their song. A large set of twenty species with 10+ recordings in each species was considered and the algorithm used time-frequency models for the characterization. Three different approaches were used in an attempt to build the foundations for the final model in a bottom to top manner. The resulting model divided birdsong into syllables that were analyzed and compared in several time-frequency domains in order to characterize the species. The domains include the Spectrogram, the Doppler domain and the Ambiguity domain. The result varied depending on the complexity of the song and the quality of the recordings but for simple songs with recordings of good quality the results were very good

    Modeling the Syntax of the song of the Great Reed Warbler Faculty of Engineering, LTH

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    The song of many songbirds can be thought of as consisting of variable sequences of a finite set of syllables. A common approach in understanding the structure of these songs is to model the syllable sequences with a Markov Model. The Markov Model can either allow one-to-one (Markov Chain), many-to-many (Hidden Markov Model) or many-to-one (Partially Observed Markov Model) state to syllable mappings. In this project the song of the Great Reed Warbler is being studied in terms of the syllable sequences (strophes) being generated. It is shown that the Markov chain captures a lot of the structure in the song in the sense that it to large degree reproduces the syllable distributions at a specific position in the song that were observed in data. The repetition distribution for some syllable classes was consistent with that of a Markov chain while other syllable classes were better modeled by allowing the self-transition probability to be adapted as the syllable class is repeated more and more. Still some other syllable classes did not have their repetition distributions accurately captured by these two alternatives

    A SVD-based classification of bird singing in different time-frequency domains using multitapers

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    In this paper, a novel method for analysing a bird’s song is presented. The song of male great reed warblers is used for developing and testing the methods. A robust method for detecting syllables is proposed and a classification of those syllables as compared to reference syllables is done. The extraction of classification features are based on the use of singular vectors in different time-frequency domains, such as the ambiguity and the doppler domains, in addition to the usual sonogram. The analysis is also made using multitaper analysis where the Welch method and the Thomson multi- tapers are compared to the more recently proposed locally stationary process multitapers

    Fast Algorithms for Sampled Multiband Signals

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    Over the past several years, computational power has grown tremendously. This has led to two trends in signal processing. First, signal processing problems are now posed and solved using linear algebra, instead of traditional methods such as filtering and Fourier transforms. Second, problems are dealing with increasingly large amounts of data. Applying tools from linear algebra to large scale problems requires the problem to have some type of low-dimensional structure which can be exploited to perform the computations efficiently. One common type of signal with a low-dimensional structure is a multiband signal, which has a sparsely supported Fourier transform. Transferring this low-dimensional structure from the continuous-time signal to the discrete-time samples requires care. Naive approaches involve using the FFT, which suffers from spectral leakage. A more suitable method to exploit this low-dimensional structure involves using the Slepian basis vectors, which are useful in many problems due to their time-frequency localization properties. However, prior to this research, no fast algorithms for working with the Slepian basis had been developed. As such, practitioners often overlooked the Slepian basis vectors for more computationally efficient tools, such as the FFT, even in problems for which the Slepian basis vectors are a more appropriate tool. In this thesis, we first study the mathematical properties of the Slepian basis, as well as the closely related discrete prolate spheroidal sequences and prolate spheroidal wave functions. We then use these mathematical properties to develop fast algorithms for working with the Slepian basis, a fast algorithm for reconstructing a multiband signal from nonuniform measurements, and a fast algorithm for reconstructing a multiband signal from compressed measurements. The runtime and memory requirements for all of our fast algorithms scale roughly linearly with the number of samples of the signal.Ph.D
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