waashk/dblp huggingface dataset:
a blind speech enhancement algorithm for the suppression of late reverberation and noise. this paper proposes a new speech enhancement algorithm for the suppression of background noise and late reverberation without a priori knowledge. a generalized spectral weighting rule based on estimations for the spectral variances of the late reverberant speech and background noise is used for speech enhancement. this allows to suppress speech distortions due to late room reflections without knowledge about the complete room impulse response. in contrast to existing methods, all needed quantities are estimated entire blindly from the reverberant and noisy speech signal. the new algorithm has also a low signal delay which is important, e.g., for speech enhancement in mobile communication devices or hearing aids.
coordination and cooperation for next generation wireless systems- overhead signalling requirements and cross layer considerations. in the evolution of wireless systems to the next generation, it is becoming increasingly important to offer substantial improvements, not only in terms of peak and average cell rates but also in terms of outage performance. to this end, interference management by means of coordination between different base stations and coverage/capacity improvements by means of cooperation with relays are considered potential candidate technologies. the introduction of multiuser mimo processing principles and cross-layer optimized resource allocation may offer promising performance gains for coordination and cooperation but their feasibility depends on overhead signaling constraints and system deployment assumptions. in this paper, we present two realistic approaches for coordination and cooperation, analyze their performance merits and discuss their complexity requirements.
a single-chip speech dialogue module and its evaluation on a personal robot, papero-mini. this paper presents a single-chip speech dialogue module and its evaluation on a personal robot. this module is implemented on an application processor that was developed primarily for mobile phones to provide a compact size, low power-consumption, and low cost. it performs speech recognition with preprocessing functions such as direction-of-arrival (doa) estimation, noise cancellation, beamforming with an array of microphones, and echo cancellation. text-to-speech (tts) conversion is also equipped with. evaluation results obtained on a new personal robot, papero-mini, which is a scale-down version of papero, demonstrate an 85% correct rate in doa estimation, and as much as 54% and 30% higher speech recognition rates in noisy environments and during robot utterances, respectively. these results are shown to be comparable to those obtained by papero.
optimization of text database using hierachical clustering. many speech and language related techniques employ models that are trained using text data. in this paper, we introduce a novel method for selecting optimized training sets from text databases. the coverage of the subset selected for training is optimized using hierarchical clustering and the generalized levenshtein distance. the validity of the proposed subset optimization technique is verified in a data-driven syllabification task. the results clearly indicate that the proposed approach meaningfully optimizes the training set, which in turn improves the quality of the trained model. compared to the existing state-of-the-art data selection technique, the proposed hierarchical clustering approach improves the compactness of data clusters, decreases the computational complexity and makes data set selection scalable. the presented idea can be used in a wide variety of language processing applications that require training with text data.
new polarimetric signal subspace detectors for sar processors. this paper deals with three new polarimetric sar processors based on subspace detectors. these algorithms aim at using models with physical and polarimetric scattering properties not exploited by the isotropic point model. these processors are implemented by computing the corresponding target subspaces. results on simulated data with realistic targets show the interest of these new processors.
acoustic model combination to compensate for residual noise in multi-channel source separation. in this paper, we propose an acoustic model combination technique for reducing a mismatch in a multi-channel noisy environment. to this end, we first apply a mask-based multi-channel source separation method, typically computational auditory scene analysis (casa), to separate the speech source from noise. however, a certain degree of noise remains in the separated speech source, especially under low signal-to-noise ratio (snr) conditions since the estimated mask is not ideal. thus, the performance of automatic speech recognition (asr) is limited. to improve asr performance, the remaining noise can be further compensated in the acoustic model domain under a framework of parallel model combination. in particular, a noise model for pmc is estimated from the noise remained after application of the maskbased source separation, and snr for pmc is also estimated based on the average of relative magnitude of mask along the utterance. it is shown from the experiments that the proposed acoustic model combination method relatively reduces the word error rate by 52.14% compared to mask-based source separation alone.
directed markov stationary features for visual classification. we investigate how to effectively incorporate spatial structure information into histogram features for boosting visual classification performance motivated by recently proposed markov stationary features (msf). first, we show that due to the symmetric property of the image occurrence modeling procedure, the stationary distribution derived from the normalized co-occurrence matrix has a trivial informative solution which only approximates the original histogram representation, i.e., does not encode proper spatial structure information. to eliminate this ambiguity, we propose in this work the so called directed markov stationary features (dmsf) to encode spatial information into histogram features, and the asymmetric essence of the co-occurrence matrices in dmsf avoids the trivial informative solutions in msf. extensive experiments on face recognition show the significant performance improvement brought by our proposed dmsf.
binaural artificial bandwidth extension (b-abe) for speech. efficient teleconference system can be created by extending the speech bandwidth and using spatial audio. if wideband speech transmission was not supported as such, an alternative way to extend the bandwidth is by exploiting an artificial bandwidth extension (abe) algorithm. in such methods the audio bandwidth is extended in the receiving end, without any transmitted extra information. when considering spatial audio it should be noted that abe methods have been developed for monaural signals and they cannot be applied as such to binaural signals due to a possible mismatch of binaural cues in the created frequency range. in this paper binaural abe, b-abe, is proposed. subjective listening tests were used to evaluate the proposed method. the results showed that the localization information is preserved well in b-abe processing.
filter-and-forward distributed beamforming for relay networks in frequency selective fading channels. a half-duplex distributed beamforming technique for relay networks with frequency selective fading channels is developed. the network relays use the filter-and-forward (ff) strategy to compensate for the transmitter-to-relay and relay-to-destination channels using finite impulse response (fir) filters. with the channel state information (csi) being available at the receiver, the transmit relay power is minimized subject to the destination quality-of-service (qos) constraint. this distributed beamforming problem is shown to have a closed-form solution. simulation results demonstrate substantial improvements in terms of the relay transmitted power and feasibility of the destination qos constraint as compared to amplify-and-forward (af) distributed beamforming techniques.
audio segmentation for speech recognition using segment features. audio segmentation is an essential preprocessing step in several audio processing applications with a significant impact e.g. on speech recognition performance. we introduce a novel framework which combines the advantages of different well known segmentation methods. an automatically estimated log-linear segment model is used to determine the segmentation of an audio stream in a holistic way by a maximum a posteriori decoding strategy, instead of classifying change points locally. a comparison to other segmentation techniques in terms of speech recognition performance is presented, showing a promising segmentation quality of our approach.
a generalized iterative water-filling algorithm for distributed power control in the presence of a jammer. consider a scenario in which k users and a jammer have a limited power budget and share a common spectrum of n orthogonal tones. the goal of each user is to allocate its power across the n tones in such a way that maximizes the total sum rate that he/she can achieve, while treating the interference of other users and the jammer's signal as additive gaussian noise. the jammer, on the other hand, wishes to allocate its power in such a way that minimizes the utility of the whole system; that being the total sum of the rates communicated over the network. for this non-cooperative game, we propose a generalized version of the existing iterative water-filling algorithm whereby the users and the jammer update their power allocations in a greedy manner. we study conditions under which the generalized iterative water-filling algorithm converges to a nash equilibrium of the game. the conditions that we derive in this paper depend only on the system parameters, and hence can be checked a priori.
on perfect channel identifiability of semiblind ml detection of orthogonal space-time block coded ofdm. this paper considers maximum-likelihood (ml) detection of orthogonal space-time block coded ofdm (ostbc-ofdm) systems without channel state information. our previous work has shown an interesting identifiability result, that the whole time-domain channel can be uniquely identified by only having one subchannel to transmit pilots. however, this identifiability is in a probability-one sense, under some mild assumptions on the channel statistics. in this paper we establish a “perfect” channel identifiability (pci) condition under which the channel is always uniquely identifiable. it is shown that pci can be achieved by judiciously applying the so-called non-intersecting subspace ostbcs. the resultant pci achieving scheme has its number of pilots larger than that used in the previous probability-one identifiability achieving scheme, but smaller than that required in conventional pilot-aided channel estimation. simulation results are presented to show that the proposed scheme can provide a better performance than the other schemes.
pushing information over acoustic channels. we propose a unidirectional communication system capable of pushing information from audio speakers to nearby audience via acoustic channels. though constrained by short transmission distances and low data throughput of sound waves, information push services based on acoustic channels can be regarded as a cost-free message delivery scheme since music playback systems and handheld devices equipped with recording capabilities have become ubiquitous in our life. furthermore, the proposed approach can provide better non-intrusiveness and more convenient user access than other auxiliary information delivery approaches like 2-d barcodes or watermarking schemes based on visual contents. to the best of our knowledge, our system is the first one that can effectively push information to receivers at feasible transmission throughput/robustness/distance within common noisy environments. various interesting applications and new business models can be facilitated with the proposed scheme.
multiple description video coding and iterative decoding of ldpca codes with side information. in this paper, we propose the use of multiple description coding to increase the robustness of distributed video coding while keeping good rate-distortion performance. the video sequence is structured into key frames and wyner-ziv frames. for each type of frame, two descriptions are generated by a multiple description scalar quantizer and sent on a loss-prone channel. when both wyner-ziv descriptions are received, they are jointly decoded along with the side information. we investigate the influence of the amount of redundancy and of the iterative decoding of the descriptions on the performance.
monaural voiced speech segregation based on elaborate harmonic grouping strategy. monaural speech segregation is a very challenging problem which has been studied by many researchers. in this paper, we focus on voiced speech segregation. different strategies are used to segregate resolved and unresolved harmonics respectively. for resolved harmonics, “harmonicity” principle and a novel mechanism based on “minimum amplitude” principle are employed. amplitude modulation rate is extracted by “enhanced” autocorrelation function of envelope to segregate unresolved harmonics which is more robust than previous method. an elaborate rule is also introduced to determine the regions dominated by resolved and by unresolved harmonics. proposed algorithm is evaluated on cooke's 100 mixtures and compared with a state-of-the-art algorithm hu and wang model. results show that proposed algorithm is more robust than the hu and wang model.
summarization of large scale social network activity. this paper presents a novel social media summarization framework. summarizing media created and shared in large scale online social networks unfolds challenging research problems. the networks exhibit heterogeneous social interactions and temporal dynamics. our proposed framework relies on the co-presence of multiple important facets: who (users), what (concepts and media), how (actions) and when (time). first, we impose a syntactic structure of the social activity (relating users, media and concepts via specific actions) in our temporal multi-graph mining algorithm. second, important activities along each facet are extracted as activity themes over time. experiments on flickr datasets demonstrate that our technique captures nontrivial evolution of media use in social networks.
minimum variance modulation filter for robust speech recognition. this paper describes a way of designing modulation filter by datadriven analysis which improves the performance of automatic speech recognition systems that operate in real environments. the filter for each nonlinear channel output is obtained by a constrained optimization process which jointly minimizes the environmental distortion as well as the distortion caused by the filter itself. recognition accuracy is measured using the cmu sphinx-iii speech recognition system, and the darpa resource management and wall street journal speech corpus for training and testing. it is shown that feature extraction followed by modulation filtering provides better performance than traditional mfcc processing under different types of background noise and reverberation.
cramér-rao bound for range estimation. in this paper, we derive the cramér-rao bound (crb) for range estimation, which does not only exploit the range information in the time delay, but also in the amplitude of the received signal. this new bound is lower than the conventional crb that only makes use of the range information in the time delay. we investigate the new bound in an additive white gaussian noise (awgn) channel with attenuation by employing both narrowband (nb) signals and ultra-wideband (uwb) signals. for nb signals, the new bound can be 3db lower than the conventional crb under certain conditions. however, there is not much difference between the new bound and the conventional crb for uwb signals. further, shadowing effects are added into the data model. several crb-like bounds for range estimation are derived to take these shadowing effects into account.
packet loss concealment based on extrapolation of speech waveform. a class of packet loss concealment algorithms for speech coding is presented. it generates the replacement waveform for the lost frame by direct extrapolation of the past speech waveform, with or without look-ahead. the itu-t g.722 appendix iii standard is based on it. when a future frame is unavailable (without look-ahead), the plc algorithm gives significantly better speech quality than g.711 appendix i - by about 0.2 pesq for high packet loss rates. when a future frame is available (with look-ahead), the plc algorithm uses the decoded speech waveform in the future frame to guide the pitch contour of waveform extrapolation during the lost frame such that the extrapolated waveform is phase-aligned with the decoded waveform after the packet loss. this technique further improved pesq by another 0.2 for high packet loss rates. the acm portal is published by the association for computing machinery. copyright © 2010 acm, inc. terms of usage privacy policy code of ethics contact us useful downloads: adobe acrobat quicktime windows media player real player
weighted nonnegative matrix factorization. nonnegative matrix factorization (nmf) is a widely-used method for low-rank approximation (lra) of a nonnegative matrix (matrix with only nonnegative entries), where nonnegativity constraints are imposed on factor matrices in the decomposition. a large body of past work on nmf has focused on the case where the data matrix is complete. in practice, however, we often encounter with an incomplete data matrix where some entries are missing (e.g., a user-rating matrix). weighted low-rank approximation (wlra) has been studied to handle incomplete data matrix. however, there is only few work on weighted nonnegative matrix factorization (wnmf) that is wlra with nonnegativity constraints. existing wnmf methods are limited to a direct extension of nmf multiplicative updates, which suffer from slow convergence while the implementation is easy. in this paper we develop relatively fast and scalable algorithms for wnmf, borrowed from well-studied optimization techniques: (1) alternating nonnegative least squares; (2) generalized expectation maximization. numerical experiments on movielens and netflix prize datasets confirm the useful behavior of our methods, in a task of collaborative prediction.
phase scrambling for blind image matching. we propose a phase-scrambling method for blind image matching, which is a direct image matching between invisible images. the phase scrambling is motivated by visual protection of images and prevention of illegal image matching. phase-only correlation (poc) can be directly applied to images protected by the proposed phase scrambling in order to estimate similarity and translation between images. poc with synchronized scrambling provides blind image matching, in which phase scrambling does not affect the accuracy of poc. the effect of visual protection and prevention of illegal image matching is evaluated through simulations to show the effectiveness of the proposed method.
instantaneous rate estimation of neuronal point processes from a compressed representation of their nonbinary spike trains. estimating rate functions underlying neural point processes is essential for characterizing the firing patterns of cortical neurons involved in sensory and motor processing. this paper introduces a new method for directly estimating neuronal firing rates from a compressed representation of their extracellular recordings. the approach is based on extending a near-optimal sparse representation of the extracellular recordings to time scales matching those of the underlying rate functions, thereby performing the same role as a kernel-density estimator but at a much lower computational cost. experimental results demonstrate that this method achieves comparable performance to the conventional gaussian kernel estimation methods at a fraction of the computational cost. this makes it suitable to brain machine interface applications where real time decoding of neural activity for neuro-motor prosthetic control using firing rate estimates is strongly desired.
dictionary learning for the sparse modelling of atrial fibrillation in ecg signals. we propose a new method for ventricular cancellation and atrial modelling in the ecg of patients suffering from atrial fibrillation. our method is based on dictionary learning. it extends both the average beat subtraction and the sparse source separation approaches. experiments on synthetic data show that this method can almost completely suppress the ventricular activity, but it generates some artifacts. contrary to other ventricular cancellations methods, our approach also learns a model for the atrial activity.
detection of short distance wireless transmitted audio. with the advance of short distance wireless technologies, it becomes popular to include fm transmission capability to mobile devices such as hands-free car kits, cellular phones with hands-free features, and mp3 players so that in addition to playing audio signal through the on-device loudspeaker, they can relay audio signal to other audio system to utilized their superior audio quality. traditionally, the switch between the on-device loudspeaker and the fm transmitter is done manually. this paper proposes an automatic and switching algorithm which leverages the aec capability already exists in many of these mobile devices. the algorithm detects the presence of audio through fm channel and switches off the on-device loudspeaker, which adds to user convenience and power saving efficiency.
kalman fitler with phase spectrum compensation algorithm for speech enhancement. in this paper, we propose to combine the kalman filter with a recent speech enhancement technique, called the phase spectrum compensation procedure, or psc. more specifically, we apply the psc technique to initialise the kalman filter, whereby psc is used to clean the noisy speech prior to lpc estimation for the kalman recursion. we refer to the combined technique as the kalman-psc filter. using an objective speech quality measure, formal subjective listening tests and spectrogram analysis, we show that the proposed method results in improved speech quality.
on separating glottal source and vocal tract information in telephony speaker verification. the popular mel-frequency cepstral coefficients (mfccs) capture a mixture of speaker-related, phonemic and channel information. speaker-related information could be further broken down according to articulatory criteria. how these underlying components are exactly mixed in the features is not well understood. to this end, in this paper we aim at separating the spectra of glottal source and vocal tract using glottal inverse filtering, with an application to speaker recognition over telephone lines. our experiments on the 10sec-10sec condition of the nist 2006 sre corpus suggest that the mel-frequency cepstrum of the voice source is not too useful for recognizing speakers. on the contrary, fusing the vocal tract spectrum with conventional mfccs improves accuracy, suggesting that vocal tract information should be enhanced.
a three-class roc for evaluating doubletalk detectors in acoustic echo cancellation. doubletalk detector (dtd) is essential to keep adaptive filter from diverging in the presence of near-end speech in acoustic echo cancellation (aec), and there was a receiver operating characteristic (roc) to characterize dtd performance. however, the traditional roc for evaluating dtd used a static time-invariant room acoustic impulse response and could not evaluate dtds which distinguish echo path change from doubletalk. we solve these problems by extending the traditional binary detection roc to three-class, and simulations show the efficiency of the proposed method.