An optimal algorithm for low power multiplierless FIR filter design using chebychev criterion

In this paper, we propose a novel finite impulse response (FIR) filter design methodology that reduces the number of operations with a motivation to reduce power consumption and enhance performance. The novelty of our approach lies in the generation of filter coefficients such that they conform to a given low-power architecture, while meeting the given filter specifications. The proposed algorithm is formulated as a mixed integer linear programming problem that minimizes chebychev error and synthesizes coefficients which consist of pre-specified alphabets. The new modified coefficients can be used for low-power VLSI implementation of vector scaling operations such as FIR filtering using computation sharing multiplier (CSHM). Simulations in 0.25um technology show that CSHM FIR filter architecture can result in 55% power and 34% speed improvement compared to carry save multiplier (CSAM) based filters.

Rapid design of discrete orthonormal wavelet transforms using silicon IP components

A rapid design methodology for orthonormal wavelet transform cores has been developed. This methodology is based on a generic, scaleable architecture utilising time-interleaved coefficients for the wavelet transform filters. The architecture has been captured in VHDL and parameterised in terms of wavelet family, wavelet type, data word length and coefficient word length. The control circuit is embedded within the cores and allows them to be cascaded without any interface glue logic for any desired level of decomposition. Case studies for stand alone and cascaded silicon cores for single and multi-stage wavelet analysis respectively are reported. The design time to produce silicon layout of a wavelet based system has been reduced to typically less than a day. The cores are comparable in area and performance to handcrafted designs. The designs are portable across a range of foundries and are also applicable to FPGA and PLD implementations.

Insults, free speech and offensiveness

This article examines what is wrong with some expressive acts, ‘insults’. Their putative wrongfulness is distinguished from the causing of indirect harms, aggregated harms, contextual harms, and damaging misrepresentations. The article clarifies what insults are, making use of work by Neu and Austin, and argues that their wrongfulness cannot lie in the hurt that is caused to those at whom such acts are directed. Rather it must lie in what they seek to do, namely to denigrate the other. The causing of offence is at most evidence that an insult has been communicated; it is not independent grounds of proscription or constraint. The victim of an insult may know that she has been insulted but not accept or agree with the insult, and thereby submit to the insulter. Hence insults need not, as Waldron argues they do, occasion dignitary harms. They do not of themselves subvert their victims' equal moral status. The claim that hateful speech endorses inequality should not be conflated with a claim that such speech directly subverts equality.

Thus, ‘wounding words’ should not unduly trouble the liberal defender of free speech either on the grounds of preventing offence or on those of avoiding dignitary harms.

A new comparator account of auditory verbal hallucinations: how motor prediction can plausibly contribute to the sense of agency for inner speech

The comparator account holds that processes of motor prediction contribute to the sense of agency by attenuating incoming sensory information and that disruptions to this process contribute to misattributions of agency in schizophrenia. Over the last 25 years this simple and powerful model has gained widespread support not only as it relates to bodily actions but also as an account of misattributions of agency for inner speech, potentially explaining the etiology of auditory verbal hallucination (AVH). In this paper we provide a detailed analysis of the traditional comparator account for inner speech, pointing out serious problems with the specification of inner speech on which it is based and highlighting inconsistencies in the interpretation of the electrophysiological evidence commonly cited in its favor. In light of these analyses we propose a new comparator account of misattributed inner speech. The new account follows leading models of motor imagery in proposing that inner speech is not attenuated by motor prediction, but rather derived directly from it. We describe how failures of motor prediction would therefore directly affect the phenomenology of inner speech and trigger a mismatch in the comparison between motor prediction and motor intention, contributing to abnormal feelings of agency. We argue that the new account fits with the emerging phenomenological evidence that AVHs are both distinct from ordinary inner speech and heterogeneous. Finally, we explore the possibility that the new comparator account may extend to explain disruptions across a range of imagistic modalities, and outline avenues for future research.

Combining multi-band and frequency-filtering techniques for speech recognition in noisy environments

While current speech recognisers give acceptable performance in carefully controlled environments, their performance degrades rapidly when they are applied in more realistic situations. Generally, the environmental noise may be classified into two classes: the wide-band noise and narrow band noise. While the multi-band model has been shown to be capable of dealing with speech corrupted by narrow-band noise, it is ineffective for wide-band noise. In this paper, we suggest a combination of the frequency-filtering technique with the probabilistic union model in the multi-band approach. The new system has been tested on the TIDIGITS database, corrupted by white noise, noise collected from a railway station, and narrow-band noise, respectively. The results have shown that this approach is capable of dealing with noise of narrow-band or wide-band characteristics, assuming no knowledge about the noisy environment.

Robust bimodal person identification using face and speech with limited training data and corruption of both modalities

This paper presents a novel method of audio-visual fusion for person identification where both the speech and facial modalities may be corrupted, and there is a lack of prior knowledge about the corruption. Furthermore, we assume there is a limited amount of training data for each modality (e.g., a short training speech segment and a single training facial image for each person). A new representation and a modified cosine similarity are introduced for combining and comparing bimodal features with limited training data as well as vastly differing data rates and feature sizes. Optimal feature selection and multicondition training are used to reduce the mismatch between training and testing, thereby making the system robust to unknown bimodal corruption. Experiments have been carried out on a bimodal data set created from the SPIDRE and AR databases with variable noise corruption of speech and occlusion in the face images. The new method has demonstrated improved recognition accuracy.

An iterative longest matching segment approach to speech enhancement with additive noise and channel distortion

This paper presents a new approach to speech enhancement from single-channel measurements involving both noise and channel distortion (i.e., convolutional noise), and demonstrates its applications for robust speech recognition and for improving noisy speech quality. The approach is based on finding longest matching segments (LMS) from a corpus of clean, wideband speech. The approach adds three novel developments to our previous LMS research. First, we address the problem of channel distortion as well as additive noise. Second, we present an improved method for modeling noise for speech estimation. Third, we present an iterative algorithm which updates the noise and channel estimates of the corpus data model. In experiments using speech recognition as a test with the Aurora 4 database, the use of our enhancement approach as a preprocessor for feature extraction significantly improved the performance of a baseline recognition system. In another comparison against conventional enhancement algorithms, both the PESQ and the segmental SNR ratings of the LMS algorithm were superior to the other methods for noisy speech enhancement.

Speech Enhancement from Additive Noise and Channel Distortion - a Corpus-Based Approach

This paper presents a new approach to single-channel speech enhancement involving both noise and channel distortion (i.e., convolutional noise). The approach is based on finding longest matching segments (LMS) from a corpus of clean, wideband speech. The approach adds three novel developments to our previous LMS research. First, we address the problem of channel distortion as well as additive noise. Second, we present an improved method for modeling noise. Third, we present an iterative algorithm for improved speech estimates. In experiments using speech recognition as a test with the Aurora 4 database, the use of our enhancement approach as a preprocessor for feature extraction significantly improved the performance of a baseline recognition system. In another comparison against conventional enhancement algorithms, both the PESQ and the segmental SNR ratings of the LMS algorithm were superior to the other methods for noisy speech enhancement. Index Terms: corpus-based speech model, longest matching segment, speech enhancement, speech recognition