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Identification of bird species based on their vocalization is studied in this paper. The main focus is introducing a new parametric representation of bird sounds for automatic identification of their species. The method is based on the statistics of local temporal patterns in bird vocalization. Two different sets of bird species are used in the classification tests. The first set contains six species that often produce inharmonic sounds. For the second set, four species that produce very different types of sounds were added. Recognition results using a k-NN-classifier shows improved recognition accuracy over the results obtained by MFCC-features.

Since modern computational devices are required to store and process increasing amounts of data generated from various sources, efficient algorithms for identification of significant information in the data are becoming essential. Sensory recordings are one example where automatic and continuous storing and processing of large amounts of data is needed. Therefore, algorithms that can alleviate the computational load of the devices and reduce their storage requirements by removing uninformative data are important. In this work we propose a method for data reduction based on theories of human attention. The method detects temporally salient events based on the context in which they occur and retains only those sections of the input signal. The algorithm is tested as a pre-processing stage in a weakly supervised keyword learning experiment where it is shown to significantly improve the quality of the codebooks used in the pattern discovery process.

Linear transformations, like wavelet transforms, and filterbanks of IIR-type and of arbitrary time-frequency plane tilings can be efficiently realized by vector ARMA models. The quality of the realization depends on how well the basis functions or impulse responses of the filterbank can be approximated by the actual VARMA based pole-zero model. The vector AR part gives an MSE-optimal block-recursive model for the target basis functions. The vector MA part is formed of the vector AR residual and further optimized by an iterative algorithm.

A linear-predictive-coding (LPC) based method for computing glottal pulses is presented. The method is based on modeling the speech production mechanism with three digital filters. The glottal contribution to the speech spectrum is first estimated with two consecutive LPC analyses. After the effect of the glottals are eliminated the vocal tract and radiation effects are modeled. Glottal waves close to the natural shape can be achieved with this straightforward method. Applications to speech coding are briefly described. >

A class of orthogonal time domain functions called famlets is introduced. Famlets are produced from their frequency domain representatives by using the inverse Fourier transform. The basic theory behind famlets and the famlet transform associated with them is formulated, and their usage in nonuniform resolution spectrum analysis is described. The similarities and dissimilarities of famlets and wavelets are discussed. >

An orthonormal set of the frequency amplitude modulated (FAM) class of functions is used to produce a frequency domain FAM transform suitable for infinite impulse response (IIR) filter design with least squared error criterion. The orthonormal basis used is chosen to be effectively implemented by a set of identical second order allpass filters equipped with a common magnitude weighting. The allpass parameters (/spl theta/, r) are first adapted to the problem in order to get rapid convergence in the mean squared error (MSE). The results show that the new method produces IIR filters with the same or better (in MSE sense) frequency domain response than finite impulse response (FIR) filters with 5 to 7 times higher order. The method allows optimization of the group delay properties. There are three different ways to approximate linear phase characteristics in the IIR filters produced. >

Many areas in audio engineering, e.g., perceptual coding, strongly rely on knowledge and models of the human auditory system. Nonuniform frequency resolution and masking phenomenon are well studied aspects. Less attention has been paid to the temporal auditory resolution. In this pilot study the temporal resolution is tested by using short (0.55-4.15 ms) clicks called Bark-FAMlets. They form an orthogonal set of signals all having identical power spectra with uniform masking properties. Individual FAMlets differ only in phase by steps called an auditory unit delay (AUD). We found that 75% discrimination is achieved when the difference between two FAMlets is 22, 11 or 10 AUDs depending on the three test conditions used.

A linear prediction process is applied to frequency warped signals. The warping is realized by using orthonormal FAM (frequency modulated complex exponentials) functions. The general formulation of WLP is given and effective realizations with allpass filters are studied. The application of auditory WLP to speech coding and speech recognition has given good results. >

A general formula for defining a wide class of orthogonal functions is given. The class is based on circular sine and cosine functions which are simultaneously frequency and amplitude modulated in such a way that they remain orthogonal. This is achieved with any choice of FM or AM function. The class, which is called FAM functions, offers a practical and flexible tool for signal processing. They have been used to produce nonuniform resolution auditory spectrograms. The achieved time-frequency resolution is of very high quality. The preliminary results show that they are approaching the theoretical limit given for the Delta f- Delta t product. The orthogonality of the FAM functions is proved, how a complex orthogonal auditory transform (OAT) can be realized by FAMs is described, and a method for constructing a complex orthogonal one Bark filter bank for signal analysis and psychoacoustic experimentation is given. >

An inherent property of many DSP algorithms is that they tend to exhibit uniform frequency resolution from zero to the Nyquist frequency. This is a direct consequence of using unit delays as building blocks; a frequency independent delay implies uniform frequency resolution. In audio applications, however, this is often an undesirable feature since the response properties are typically specified and measured on a logarithmic scale, following the behavior of the human auditory system. We present an overview of warped filters and DSP techniques which can be designed to better match the audio and auditory criteria. Audio applications, including modeling of auditory and musical phenomena, equalization techniques, auralization, and audio coding, are presented.

The conventional theory of linear prediction (LP) is renewed and extended to form a more flexible algorithm called generalized linear prediction (GLP). There are three new levels of generalization available. On the first level (I) the predictor FIR is replaced with a generalized FIR constructed out of allpass sections having complex coefficients. On the second level (II) the allpass filters have distributed coefficients, i.e., they are unequal, and on the third and the most general level (III) the filter sections may have different characteristics. The theory of GLP is presented and the algorithm is tested with speech signals. The results show that GLP works as desired: nonuniform frequency resolution can be achieved and the resolution is controlled by the choice of the allpass parameters. On level I, the angle of the pole-zero-pair of the allpass sections defines the highest resolution area while the radius of the pole controls the degree of the resolution improvement. The GLP prediction error decreases rapidly with the order of the predictor. Its normalized RMS value falls off exponentially and its spectral flatness improves efficiently. On the average the results are clearly better than those of conventional LP. Levels II and III are only briefly discussed.

A new method to realize arbitrary time-frequency plane tilings together with critical sampling in block-recursive filterbanks is presented. The method leads to pole-zero approximation of the target channel transfer functions. Perfect reconstruction within the limits of the approximation error can be achieved.

In low-delay audio coding (coding delay

Algorithms that can be used to synthesize guitar sounds on a floating-point signal processor are presented. A finite impulse response (FIR) Lagrange interpolator is introduced to implement the efficient and precise fractional delay approximation that is needed to achieve arbitrary and varying-length strings. This kind of interpolation is especially good in avoiding distortion and undesirable extra effects when the string length is changing continuously during the synthesis of a sound. The interpolator can also be used in other cases, e.g. in transmission-line modeling of acoustic tube resonators in wind instruments and for vocal tract models in speech synthesis. In addition to the interpolation principle, the implementation of the guitar string model on the TMS320C30 floating-point signal processor is described. >

This paper will address the problem of modeling the acoustic guitar for real-time synthesis on signal processairs. We will present a scheme for modeling the string for high-quality sound synthesis when the length of Ihe string is changing dynamically. We will focus also on the problem of modeling the body of the guitar for real-time synthesis. Filter-based approaches were experimented by LPC estimation, IIR-filter synthesis and FIR-filter approximation. Perceptual evaluation was used and taken into account. Real-time synthesis was implemented on the TMS32OC30 floating-point signal processor. The presentation includes audio examples. Introduction Computational modeling of musical instruments is an alternative to commonly used and more straightforward sound synthesis techniques like FA4 synthesis and waveform sampling. The traditional,approach 10 efficient modeling of a vibrating string has been to use proper digital filters or transmission lines, see e.g. Kauplus and Strong [l] and its extensions by Jaffe and Smith [2]. These represent "semiphysical" modeling where only some of the most fundamental features of the string, especially the transmission line property, are retained to achieve efficient computation. More complete finite element models and other kinds of physical modeling may lead to very realistic sounds but tend to be computationally too expensive for real-time purposes. Modeling of the guitar body for real-time sound synthesis seems too difficult unless a digital filter approach to approximate the transfer function is used. The derivation of the detailed transfer function from mechanical and acoustical parameters seems impossible. The remaining choice is to estimate the transfer function filter from measurements of a real guitar or to design a filter that approximates the general properties of the real guiltar body. In addition to strings and body the interactions between them (at least between the strings) should be included. String Modeling The natural way of modeling a guitar string is to describe it as a two-directional transmission or delay line (see Fig. la.) where the vibrational waves travel in both directions, reflecting at both ends. If all losses and other nonidealities are reduced to the reflection filters at the end points the computation of the ideal string is efficient by using two delay lines. The next problem is how to approximate the fractional part of the delay to achieve any (non-integer) length of the delay Wine. Allpass filters [2] are considered as a good solution if the string length is fixed. If the length is dynamically varying, however, it is very difficult to avoid transients and glitches when Ihe integer part of the delay line must change its length. E x c i t a t i d po in t