A Phase Vocoder based on Nonstationary Gabor Frames

We propose a new algorithm for time stretching music signals based on the theory of nonstationary Gabor frames (NSGFs). The algorithm extends the techniques of the classical phase vocoder (PV) by incorporating adaptive time-frequency (TF) representations and adaptive phase locking. The adaptive TF representations imply good time resolution for the onsets of attack transients and good frequency resolution for the sinusoidal components. We estimate the phase values only at peak channels and the remaining phases are then locked to the values of the peaks in an adaptive manner. During attack transients we keep the stretch factor equal to one and we propose a new strategy for determining which channels are relevant for reinitializing the corresponding phase values. In contrast to previously published algorithms we use a non-uniform NSGF to obtain a low redundancy of the corresponding TF representation. We show that with just three times as many TF coefficients as signal samples, artifacts such as phasiness and transient smearing can be greatly reduced compared to the classical PV. The proposed algorithm is tested on both synthetic and real world signals and compared with state of the art algorithms in a reproducible manner.Comment: 10 pages, 6 figure

Audio- ja puhesignaalien aika-asteikon muuttaminen

In audio time-scale modification (TSM), the duration of an audio recording is changed while retaining its local frequency content. In this thesis, a novel phase vocoder based technique for TSM was developed, which is based on the new concept of fuzzy classification of points in the time-frequency representation of an input signal. The points in the time-frequency representation are classified into three signal classes: tonalness, noisiness, and transientness. The information from the classification is used to preserve the distinct nature of these components during modification. The quality of the proposed method was evaluated by means of a listening test. The proposed method scored slightly higher than a state-of-the-art academic TSM technique, and similarly as a commercial TSM software. The proposed method is suitable for high-quality TSM of a wide variety of audio and speech signals.Äänen aika-asteikon muuttamisessa äänitteen pituutta muokataan niin, että sen paikallinen taajuussisältö säilyy samanlaisena. Tässä diplomityössä kehitettiin uusi, vaihevokooderiin pohjautuva menetelmä äänen aika-asteikon muuttamiseen. Menetelmä perustuu äänen aikataajuusesityksen pisteiden sumeaan luokitteluun. Pisteet luokitellaan soinnillisiksi, kohinaisiksi ja transienttisiksi määrittämällä jatkuva totuusarvo pisteen kuulumiselle kuhunkin näistä luokista. Sumeasta luokittelusta saatua tietoa käytetään hyväksi näiden erilaisten signaalikomponenttien ominaisuuksien säilyttämiseen aika-asteikon muuttamisessa. Esitellyn menetelmän laatua arvioitiin kuuntelukokeen avulla. Esitelty menetelmä sai kokeessa hieman paremmat pisteet kuin viimeisintä tekniikkaa edustava akateeminen menetelmä, ja samanlaiset pisteet kuin kaupallinen ohjelmisto. Esitelty menetelmä soveltuu monenlaisien musiikki- ja puhesignaalien aika-asteikon muuttamiseen

Single-trial multiwavelet coherence in application to neurophysiological time series

A method of single-trial coherence analysis is presented, through the application of continuous muldwavelets. Multiwavelets allow the construction of spectra and bivariate statistics such as coherence within single trials. Spectral estimates are made consistent through optimal time-frequency localization and smoothing. The use of multiwavelets is considered along with an alternative single-trial method prevalent in the literature, with the focus being on statistical, interpretive and computational aspects. The multiwavelet approach is shown to possess many desirable properties, including optimal conditioning, statistical descriptions and computational efficiency. The methods. are then applied to bivariate surrogate and neurophysiological data for calibration and comparative study. Neurophysiological data were recorded intracellularly from two spinal motoneurones innervating the posterior,biceps muscle during fictive locomotion in the decerebrated cat

Full Band All-Sky Search for Periodic Gravitational Waves in the O1 LIGO Data

We report on a new all-sky search for periodic gravitational waves in the frequency band 475-2000 Hz and with a frequency time derivative in the range of [-1.0,+0.1] x 10-8 Hz/s. Potential signals could be produced by a nearby spinning and slightly nonaxisymmetric isolated neutron star in our Galaxy. This search uses the data from Advanced LIGO\u27s first observational run O1. No gravitational-wave signals were observed, and upper limits were placed on their strengths. For completeness, results from the separately published low-frequency search 20-475 Hz are included as well. Our lowest upper limit on worst-case (linearly polarized) strain amplitude h0 is ∼4 x 10-25 near 170 Hz, while at the high end of our frequency range, we achieve a worst-case upper limit of 1.3 x 10-24. For a circularly polarized source (most favorable orientation), the smallest upper limit obtained is ∼1.5 x 10-25

Full band all-sky search for periodic gravitational waves in the O1 LIGO data

We report on a new all-sky search for periodic gravitational waves in the frequency band 475–2000 Hz and with a frequency time derivative in the range of [−1.0,+0.1]×10^(−8)  Hz/s. Potential signals could be produced by a nearby spinning and slightly nonaxisymmetric isolated neutron star in our Galaxy. This search uses the data from Advanced LIGO’s first observational run O1. No gravitational-wave signals were observed, and upper limits were placed on their strengths. For completeness, results from the separately published low-frequency search 20–475 Hz are included as well. Our lowest upper limit on worst-case (linearly polarized) strain amplitude h_0 is ∼4×10^(−25) near 170 Hz, while at the high end of our frequency range, we achieve a worst-case upper limit of 1.3×10^(−24). For a circularly polarized source (most favorable orientation), the smallest upper limit obtained is ∼1.5×10^(−25)

Signal Processing and Propagation for Aeroacoustic Sensor Networking,” Ch

Passive sensing of acoustic sources is attractive in many respects, including the relatively low signal bandwidth of sound waves, the loudness of most sources of interest, and the inherent difficulty of disguising or concealing emitted acoustic signals. The availability of inexpensive, low-power sensing and signal-processing hardware enables application of sophisticated real-time signal processing. Among th