Filter algorithm based on cochlear mechanics and neuron filter mechanism and application on enhancement of audio signals

A filter algorithm based on cochlear mechanics and neuron filter mechanism is proposed from the view point of vibration.It helps to solve the problem that the non-linear amplification is rarely considered in studying the auditory filters.A cochlear mechanical transduction model is built to illustrate the audio signals processing procedure in cochlea,and then the neuron filter mechanism is modeled to indirectly obtain the outputs with the cochlear properties of frequency tuning and non-linear amplification.The mathematic description of the proposed algorithm is derived by the two models.The parameter space,the parameter selection rules and the error correction of the proposed algorithm are discussed.The unit impulse responses in the time domain and the frequency domain are simulated and compared to probe into the characteristics of the proposed algorithm.Then a 24-channel filter bank is built based on the proposed algorithm and applied to the enhancements of the audio signals.The experiments and comparisons verify that,the proposed algorithm can effectively divide the audio signals into different frequencies,significantly enhance the high frequency parts,and provide positive impacts on the performance of speech enhancement in different noise environments,especially for the babble noise and the volvo noise.

An Efficient Approach for Segmentation, Feature Extraction and Classification of Audio Signals

Due to the presence of non-stationarities and discontinuities in the audio signal, segmentation and classification of audio signal is a really challenging task. Automatic music classification and annotation is still considered as a challenging task due to the difficulty of extracting and selecting the optimal audio features. Hence, this paper proposes an efficient approach for segmentation, feature extraction and classification of audio signals. Enhanced Mel Frequency Cepstral Coefficient (EMFCC)-Enhanced Power Normalized Cepstral Coefficients (EPNCC) based feature extraction is applied for the extraction of features from the audio signal. Then, multi-level classification is done to classify the audio signal as a musical or non-musical signal. The proposed approach achieves better performance in terms of precision, Normalized Mutual Information (NMI), F-score and entropy. The PNN classifier shows high False Rejection Rate (FRR), False Acceptance Rate (FAR), Genuine Acceptance rate (GAR), sensitivity, specificity and accuracy with respect to the number of classes.

A microfluidic biosensor for multiplex immunoassay of foodborne pathogens agitated by programmed audio signals

Foods are often contaminated by multiple foodborne pathogens,which threatens human health.In this work,we developed a microfluidic biosensor for multiplex immunoassay of foodborne bacteria with agitation driven by programmed audio signals.This agitation,powered by the vibration of a speaker cone during music playing,accelerated the mass transport in the incubation process to form bacterial complexes within 10 min.Immunoassay reagents of the two target bacteria(Escherichia coli O157:H7 and Salmonella typhimurium)were preloaded into the corresponding fore-vacuum storage chamber on the chip,and released to participate in the subsequent immune analysis process by piercing the chambers.All the detection processes were integrated into a single microfluidic chip and controlled by a smartphone through Bluetooth.Under selected conditions,wide linear ranges and low limits of detection(LODs<2CFU/m L)were obtained,and real food samples were successfully determined within 30 min.This biosensing method can be extended to wide-ranging applications by loading different recognizing reagents.

Determined Reverberant Blind Source Separation of Audio Mixing Signals

Audio signal separation is an open and challenging issue in the classical“Cocktail Party Problem”.Especially in a reverberation environment,the separation of mixed signals is more difficult separated due to the influence of reverberation and echo.To solve the problem,we propose a determined reverberant blind source separation algorithm.The main innovation of the algorithm focuses on the estimation of the mixing matrix.A new cost function is built to obtain the accurate demixing matrix,which shows the gap between the prediction and the actual data.Then,the update rule of the demixing matrix is derived using Newton gradient descent method.The identity matrix is employed as the initial demixing matrix for avoiding local optima problem.Through the real-time iterative update of the demixing matrix,frequency-domain sources are obtained.Then,time-domain sources can be obtained using an inverse short-time Fourier transform.Experi-mental results based on a series of source separation of speech and music mixing signals demonstrate that the proposed algorithm achieves better separation performance than the state-of-the-art methods.In particular,it has much better superiority in the highly reverberant environment.

Design and Simulation of an Audio Signal Alerting and Automatic Control System

A large part of our daily lives is spent with audio information. Massive obstacles are frequently presented by the colossal amounts of acoustic information and the incredibly quick processing times. This results in the need for applications and methodologies that are capable of automatically analyzing these contents. These technologies can be applied in automatic contentanalysis and emergency response systems. Breaks in manual communication usually occur in emergencies leading to accidents and equipment damage. The audio signal does a good job by sending a signal underground, which warrants action from an emergency management team at the surface. This paper, therefore, seeks to design and simulate an audio signal alerting and automatic control system using Unity Pro XL to substitute manual communication of emergencies and manual control of equipment. Sound data were trained using the neural network technique of machine learning. The metrics used are Fast Fourier transform magnitude, zero crossing rate, root mean square, and percentage error. Sounds were detected with an error of approximately 17%;thus, the system can detect sounds with an accuracy of 83%. With more data training, the system can detect sounds with minimal or no error. The paper, therefore, has critical policy implications about communication, safety, and health for underground mine.

Reception of infrasound and audio current in derma nerves

Determining the frequency range of derma nerve that responds to audio current is fundamental for the development of skin-hearing technology. Previous studies have shown that the range of derma nerve responding to audio current is 15-15 000 Hz, because audio amplification is not separated from the step-up transformer. Therefore, the present study used a signal generator which directly drives plane electrodes, simplified the original experimental environment for skin-hearing, measured lower limit voltage of frequency for derma nerve receiving pulse current signals, and revealed that the frequency range of human derma nerve response was as wide as 0.1-30 000 Hz. Results demonstrate that human derma nerve receives audio signals and infrasound within a wide frequency range.

A Novel Multichannel Audio Signal Compression Method Based on Tensor Representation and Decomposition

Multichannel audio signal is more difficult to be compressed than mono and stereo ones.A novel multichannel audio signal compression method based on tensor representation and decomposition is proposed in this paper.The multichannel audio is represented with 3-order tensor space and is decomposed into core tensor with three factor matrices in the way of channel,time and frequency.Only the truncated core tensor is transmitted which will be multiplied by the pre-trained factor matrices to reconstruct the original tensor space.Objective and subjective experiments have been done to show a very noticeable compression capability with an acceptable output quality.The novelty of the proposed compression method is that it enables both high compression capability and backward compatibility with limited signal distortion to the hearing.

STUDY ON AUDIO INFORMATION HIDING METHOD BASED ON MODIFIED PHASE PARTITION

Hiding efficiency of traditional audio information hiding methods is always low since the sentience similarity cannot be guaranteed. A new audio information hiding method is proposed in this letter which can impose the insensitivity with the audio phase for auditory and realize the information hiding through specific algorithm in order to modify local phase within the auditory perception. The algorithm is to introduce the operation of "set 1" and "set 0" for every phase vectors, then the phases must lie on the boundary of a phase area after modified. If it lies on "1" boundary, it comes by set 1 operation. If it lies on "0" boundary, it comes by set 0 operation. The results show that, compared with the legacy method, the proposed method has better auditory similarity, larger information embedding capacity and lower code error rate. As a kind of blind detect method, it fits for application scenario without channel interference.

Audio Mixing Inversion via Embodied Self-supervised Learning

Audio mixing is a crucial part of music production.For analyzing or recreating audio mixing,it is of great importance to conduct research on estimating mixing parameters used to create mixdowns from music recordings,i.e.,audio mixing inversion.However,approaches of audio mixing inversion are rarely explored.A method of estimating mixing parameters from raw tracks and a stereo mixdown via embodied self-supervised learning is presented.In this work,several commonly used audio effects including gain,pan,equalization,reverb,and compression,are taken into consideration.This method is able to learn an inference neural network that takes a stereo mixdown and the raw audio sources as input and estimate mixing parameters used to create the mixdown by iteratively sampling and training.During the sampling step,the inference network predicts a set of mixing parameters,which is sampled and fed to an audio-processing framework to generate audio data for the training step.During the training step,the same network used in the sampling step is optimized with the sampled data generated from the sampling step.This method is able to explicitly model the mixing process in an interpretable way instead of using a black-box neural network model.A set of objective measures are used for evaluation.The experimental results show that this method has better performance than current state-of-the-art methods.

A novel demodulation method for transmission using nitrogen–vacancy-based solid-state quantum sensor

Diamond based quantum sensing is a fast-emerging field with both scientific and technological significance.The nitrogen–vacancy(NV)center,a crystal defect in diamond,has become a unique object for microwave sensing applications due to its excellent stability,long spin coherence time,and optical properties at ambient condition.In this work,we use diamond NV center as atomic receiver to demodulate on–off keying(OOK)signal transmitted in broad frequency range(2 GHz–14 GHz in a portable benchtop setup).We proposed a unique algorithm of voltage discrimination and demonstrated audio signal transceiving with fidelity above 99%.This diamond receiver is attached to the end of a tapered fiber,having all optic nature,which will find important applications in data transmission tasks under extreme conditions such as strong electromagnetic interference,high temperatures,and high corrosion.

Identical-video retrieval using the low-peak feature of a video's audio information

The recognition and retrieval of identical videos by combing through entire video files requires a great deal of time and memory space. Therefore, most current video-matching methods analyze only a part of each video's image frame information. All these methods, however, share the critical problem of erroneously categorizing identical videos as different if they have merely been altered in resolution or converted with a different codec. This paper deals instead with an identical-video-retrieval method using the low-peak feature of audio data. The low-peak feature remains relatively stable even with changes in bit-rate or codec. The proposed method showed a search success rate of 93.7% in a video matching experiment. This approach could provide a technique for recognizing identical content on video file share sites.

An algorithm that minimizes audio fingerprints using the difference of Gaussians

Recently,many audio search sites headed by Google have used audio fingerprinting technology to search for the same audio and protect the music copyright using one part of the audio data.However,if there are fingerprints per audio file,then the amount of query data for the audio search increases.In this paper,we propose a novel method that can reduce the number of fingerprints while providing a level of performance similar to that of existing methods.The proposed method uses the difference of Gaussians which is often used in feature extraction during image signal processing.In the experiment,we use the proposed method and dynamic time warping and undertake an experimental search for the same audio with a success rate of 90%.The proposed method,therefore,can be used for an effective audio search.