Spectral analysis of the call of the male killer whale

Singleton, Richard C.; Poulter, Thomas C.

doi:10.1109/tau.1967.1161898

Cited by 37 publications

(11 citation statements)

References 8 publications

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“…A 10% cosine-bell data window, weights for which are defined by BRAULT and WHITE (1971), was applied to avoid the undesirable effects due to finite length of the data sample, before the discrete Fourier transforms were obtained. From the Fourier cosine and sine coefficients, ak and bk at each frequency k (k=0, 1,..., N/2), the raw spectral estimates (a+b)/2 k kwere computed which were then smoothed by application of weights 0.25, 0.5 and 0.25 (SINGLETON and POULTER, 1967). Amplitudes are derived from the relation 2(power)12 While both the amplitude and phase of the annual component show considerable variation over the 24 local hours, the phase of the semiannual component is nearly same at all hous of the day.…”

Section: Discussionmentioning

confidence: 99%

On the local time dependence of annual and semiannual modulations of geomagnetic disturbance field.

Arora

Rangarajan

1974

J. geomagn. geoelec

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Section: Discussionmentioning

confidence: 99%

On the local time dependence of annual and semiannual modulations of geomagnetic disturbance field.

Arora

Rangarajan

1974

J. geomagn. geoelec

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“…There was a one point overlap between the two segments. After ea.ch segment was multiplied by the Hanning window [15], the fast Fourier transform (FFT) was used to compute the discrete Fourier transform (DFT) of the m data points [63.…”

Section: B Initial Orthogonal Basismentioning

confidence: 99%

Word recognition by means of orthogonal functions

Clark

1970

IEEE Trans. Audio Electroacoust.

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This paper describes experiments in which word recognition is based on comparing the projections of input words on an orthogonal basis with those of a stored library of words. An initial orthogonal basis is determined from the generalized spectrum of short time segments selected from a vocabulary of ten words. The initial basis is optimized by minimizing the complementary error energy. By projecting a spoken word onto the optimum orthogonal basis, a sequence of numbers is generated to represent the word. By correlating the absolute values of the sequence with those of a stored library of words, the spoken word is identifed. The percent of correct recognition varies from 71.6 to 96.6 percent for two speakers.Techniques are developed to improve the recognition scores and to reduce the lengthy computer processing time and large storage requirement. First a master template is made for each word by averaging six templates for the particular word. For one speaker the percent of correct recognition increases to 100 percent when incoming words are compared against the master templates. For a second speaker, the recognition rates improve significantly and vary between 93 and 98 percent when the master templates are used. To further improve the recognition process, the feasibility of grouping words into several classes is demonstrated. The classifications are based on the locations of formant regions and the time durations of each spoken word,

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“…The %chnique has been applied to vocoding [2,3], digital filtering [4], analysis of underwater sound recordings [5], analysis of electroencephalographic data [6], and many other areas. Because of this wide range of application, several groups have proposed hardware implementations of the algorithm [7][8][9].…”

Section: Introductionmentioning

confidence: 99%

Numerical Analysis: A fast fourier transform algorithm for real-valued series

Bergland¹

1968

Commun. ACM

129

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A new procedure is presented for calculating the complex, discrete Fourier transform of real-valued time series. This procedure is described for an example where the number of points in the series is an integral power of two. This algorithm preserves the order and symmetry of the Cooley-Tukey fast Fourier transform algorithm while effecting the two-to-one reduction in computation and storage which can be achieved when the series is real. Also discussed are hardware and software implementations of the algorithm which perform only (N/4) log2 (N/2) complex multiply and add operations, and which require only N real storage locations in analyzing each N-point record.KEY WORDS AND PHRASES: fast Fourier transform, time series analysis, digital filtering, spectral analysis, real-tlme spectrum analyzers, Fourier analysis, discrete Fourier transform, digital spectrum analysis, Fourier analysis algorithm, Fourier synthesis algorithm CR CATEGORIES: 3.80, 3.81, 4.9, 5.49, 6.22 [10][11][12][13][14]; and improving the computational efficiency when the input series consists solely of real numbers [14,15].There have been two basic approaches to the evaluation of real-valued time series. The first approach makes use of the conventional complex FFT algorithm and depends upon forming an artificial N/2-term complex record from each N-term real record [15]. A more direct approach has recently been proposed by Edson [16] in which he suggests specializing the complex FFT algorithm by eliminating those computations which lead to redundant results. The algorithm discussed in this paper extends this latter approach not only to decrease the required computation but also to preserve the order and symmetry which made the original complex Cooley-Tukey algorithm so convenient to implement in both hardware and software. Redundancy in the Cooley-Tukey AlgorithmAn interpretation of the A~ values of the Cooley-Tukey algorithm for i = 0, 1, 2,-.., m, where N = 2 m, has been noted by Cooley [17] and described in detail by Shively [7] and others [18]. This interpretation describes the A i values at each stage as being sets of unnormalized Fourier coefficients formed from interleaved sets of samples. Although this concept can be conveniently generalized to apply when N = rlr2.., r,~, an example ~vill be carried through for N = 2% In this case the original samples (i.e. the A0 values) can be thought of as N distinct one-term Fourier series representations of the dc (direct current) value of the time function. In Figure 1 704

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Spectral analysis of the call of the male killer whale

Cited by 37 publications

References 8 publications

On the local time dependence of annual and semiannual modulations of geomagnetic disturbance field.

On the local time dependence of annual and semiannual modulations of geomagnetic disturbance field.

Word recognition by means of orthogonal functions

Numerical Analysis: A fast fourier transform algorithm for real-valued series

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