A convolutional neural-network model of human cochlear mechanics and filter tuning for real-time applications

Auditory models are commonly used as feature extractors for automatic speech-recognition systems or as front-ends for robotics, machine-hearing and hearing-aid applications. Although auditory models can capture the biophysical and nonlinear properties of human hearing in great detail, these biophysical models are computationally expensive and cannot be used in real-time applications. We present a hybrid approach where convolutional neural networks are combined with computational neuroscience to yield a real-time end-to-end model for human cochlear mechanics, including level-dependent filter tuning (CoNNear). The CoNNear model was trained on acoustic speech material and its performance and applicability were evaluated using (unseen) sound stimuli commonly employed in cochlear mechanics research. The CoNNear model accurately simulates human cochlear frequency selectivity and its dependence on sound intensity, an essential quality for robust speech intelligibility at negative speech-to-background-noise ratios. The CoNNear architecture is based on parallel and differentiable computations and has the power to achieve real-time human performance. These unique CoNNear features will enable the next generation of human-like machine-hearing applications

Biophysically-inspired features improve the generalizability of neural network-based speech enhancement systems

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SERGAN : speech enhancement using relativistic generative adversarial networks with gradient penalty

Popular neural network-based speech enhancement systems operate on the magnitude spectrogram and ignore the phase mismatch between the noisy and clean speech signals. Recently, conditional generative adversarial networks (cGANs) have shown promise in addressing the phase mismatch problem by directly mapping the raw noisy speech waveform to the underlying clean speech signal. However, stabilizing and training cGAN systems is difficult and they still fall short of the performance achieved by spectral enhancement approaches. This paper introduces relativistic GANs with a relativistic cost function at its discriminator and gradient penalty to improve time-domain speech enhancement. Simulation results show that relativistic discriminators provide a more stable training of cGANs and yield a better generator network for improved speech enhancement performance

Real-time audio processing on a raspberry Pi using deep neural networks

Over the past years, deep neural networks (DNNs) have quickly grown into the state-of-the-art technologyfor various machine learning tasks such as image and speech recognition or natural language processing.However, as DNN-based applications typically require significant amounts of computation, running DNNson resource-constrained devices still constitutes a challenge, especially for real-time applications such aslow-latency audio processing. In this paper, we aimed to perform real-time noise suppression on a low-costembedded platform with limited resources, using a pre-trained DNN-based speech enhancement model. Aportable setup was employed, consisting of a Raspberry Pi 3 Model B+ fitted with a soundcard and head-phones. A (basic) low-latency Python framework was developed to accommodate an audio processing al-gorithm operating in a real-time environment. Various layouts and trainable parameters of the DNN-basedmodel as well as different processing time intervals (from 64 up to 8 ms) were tested and compared usingobjective metrics (e.g. PESQ, segSNR) to achieve the best possible trade-off between noise suppressionperformance and audio latency. We show that 10-layer DNNs with up to 350,000 trainable parameters cansuccessfully be implemented on the Raspberry Pi 3 Model B+ and yield latencies below 16-ms for real-timeaudio applications

Hearing-impaired bio-inspired cochlear models for real-time auditory applications

Biophysically realistic models of the cochlea are based on cascaded transmission-line (TL) models which capture longitudinal coupling, cochlear nonlinearities, as well as the human frequency selectivity. However, these models are slow to compute (order of seconds/minutes) while machine-hearing and hearing-aid applications require a real-time solution. Consequently, real-time applications often adopt more basic and less time-consuming descriptions of cochlear processing (gamma-tone, dual resonance nonlinear) even though there are clear advantages in using more biophysically correct models. To overcome this, we recently combined nonlinear Deep Neural Networks (DNN) with analytical TL cochlear model descriptions to build a real-time model of cochlear processing which captures the biophysical properties associated with the TL model. In this work, we aim to extend the normal-hearing DNN-based cochlear model (CoNNear) to simulate frequency-specific patterns of hearing sensitivity loss, yielding a set of normal and hearing-impaired auditory models which can be computed in real-time and are differentiable. They can hence be used in backpropagation networks to develop the next generation of hearing-aid and machine hearing applications

Antimicrobial effects of Citrus sinensis peel extracts against dental caries bacteria: an in vitro study

Background: Ethnomedicine is gaining admiration since years but still there is abundant medicinal flora which is unrevealed through research. The study was conducted to assess the in vitro antimicrobial potential and also determine the minimum inhibitory concentration (MIC) of Citrus sinensis peel extracts with a view of searching a novel extract as a remedy for dental caries pathogens. Material and Methods: Aqueous and ethanol (cold and hot) extracts prepared from peel of Citrus sinensis were screened for in vitro antimicrobial activity against Streptococcus mutans and Lactobacillus acidophilus , using agar well diffusion method. The lowest concentration of every extract considered as the minimal inhibitory concentration (MIC) values were determined for both test organisms. One way ANOVA with Post Hoc Bonferroni test was applied for statistical analysis. Confidence level and level of significance were set at 95% and 5% respectively. Results: Dental caries pathogens were inhibited most by hot ethanolic extract of Citrus sinensis peel followed by cold ethanolic extract. Aqueous extracts were effective at very high concentrations. Minimum inhibitory concentration of hot and cold ethanolic extracts of Citrus sinensis peel ranged between 12-15 mg/ml against both the dental caries pathogens. Conclusions: Citrus sinensis peels extract was found to be effective against dental caries pathogens and contain compounds with therapeutic potential. Nevertheless, clinical trials on the effect of these plants are essential before advocating large-scale therap

Positive impacts of integrating flaxseed meal as a potential feed supplement in livestock and poultry production: Present scientific understanding

When it comes to food and fiber production, flaxseed (Linum usitatissimum) has been around the longest. Oil makes up over 41% of a flaxseed's total weight; of that, more than 70% is polyunsaturated. Protein, dietary fiber, α-linolenic acid (ALA), flaxseed gum, and many other beneficial compounds are abundant in flaxseed meal (FSM). There is as much as 30% crude protein in FSM. Therefore, FSM can serve as a source of excellent protein for livestock. FSM increases the efficiency and effectiveness of livestock and poultry farming. FSM can be used as an essential protein feed component in cattle and poultry farming, boosting production and profitability. Because it contains anti-nutritional ingredients such as cyanogenic glycosides, tannins, phytic acid, oxalic acid and an anti-vitamin B6 factor, the use of FSM in livestock and poultry diets is restricted. Animal nutritionists have recently shown a growing interest in reducing anti-nutritional elements and boosting FSM's nutritional value. Recently, fermented FSM has been used to feed cattle and poultry; hence its dietary benefits have not yet been fully assessed. The present article, therefore, addresses the chemical make-up, bioactive components, anti-nutritional aspects, and positive impacts of FSM in livestock and poultry production