Received Audio Signal Research Articles

MONITORING THE ABSENCE OF QUEEN BEE IN THE HIVE USING DEEP LEARNING AND HILBERT HUANG TRANSFORM

In this paper, we present a fusion method to monitor the absence of the queen bee in a hive using a combination of deep learning neural networks, support vector machine (SVM), and Hilbert Huang transform. First, we collect the sound data from the hive in the presence and missing of the queen bee using the Internet of Things system (IoT). Next, we slice the received audio signal into small chunk with a duration of 10 seconds. In the next step, we perform the Hilbert Huang Transform on each chunks to obtain the spectral image of the audio signal with and without the queen bee. Finally, we use the obtained spectral images to train and test the deep learning neural networks model combined with a support vector machine (SVM) to classify the spectral image of the audio signal with and without the queen bee. The test results on the test set achieved a classification accuracy of 98.61%.

The IoT based embedded system for the detection and discrimination of animals to avoid human–wildlife conflict

Human–wildlife conflict (HWC) is one of the major crises in the valparai region of Anamalai Tiger Reserves (ATR). It is essential to reduce the HWC to save people from the wildlife and also to protect wildlife. In this paper, we propose an automated unsupervised system for the identification and classification of animals from their acoustic signal. The environment sound signals are captured using a microphone and the audio is stored in a.wav file and is sent to a base station through a radio frequency (RF) network. This system is processed with three steps (i) from the received audio signal initially, animal identification is done by extracting features of an animal signal using Mel frequency cepstral coefficient (MFCC) and classification of animal is performed by radial basis function (RBF) neural network, (ii) age estimation (calf/adult) is performed by autocorrelation, (iii) elephant state of mind (SOM) is detected by extracting features of an elephant acoustic signal using gammatone frequency cepstral coefficient (GFCC) and classification of various sounds of elephant are performed by support vector machine (SVM). Based on this, an early warning message which contains an animal type, age (calf/adult), elephant SOM, global positioning system (GPS) tracks its location information and all this information will be transmitted via global system for mobile communication (GSM) to the forest authorities, local communities, radio station or local channels indicating that an animal movement is near to forest border areas. The results were fed into a separate web page using the internet of thing (IoT).

Patchwork-Based Audio Watermarking Method Robust to De-synchronization Attacks

This paper presents a patchwork-based audio watermarking method to resist de-synchronization attacks such as pitch-scaling, time-scaling, and jitter attacks. At the embedding stage, the watermarks are embedded into the host audio signal in the discrete cosine transform (DCT) domain. Then, a set of synchronization bits are implanted into the watermarked signal in the logarithmic DCT (LDCT) domain. At the decoding stage, we analyze the received audio signal in the LDCT domain to find the scaling factor imposed by an attack. Then, we modify the received signal to remove the scaling effect, together with the embedded synchronization bits. After that, watermarks are extracted from the modified signal. Simulation results show that at the embedding rate of 10 bps, the proposed method achieves 98.9% detection rate on average under the considered de-synchronization attacks. At the embedding rate of 16 bps, it can still obtain 94.7% detection rate on average. So, the proposed method is much more robust to de-synchronization attacks than other patchwork watermarking methods. Compared with the audio watermarking methods designed for tackling de-synchronization attacks, our method has much higher embedding capacity.

A blind digital audio watermarking scheme based on EMD and UISA techniques

In this paper, a new perceptual spread spectrum audio watermarking scheme is discussed. The watermark embedding process is performed in the Empirical Mode Decomposition (EMD) domain, and the hybrid watermark extraction process is based on the combination of EMD and ISA (Independent Subspace Analysis) techniques, followed by the generic detection system, i.e. inverse perceptual filter, predictor filter and correlation based detector. Since the EMD decomposes the audio signal into several oscillating components---the intrinsic mode functions (IMF)---the watermark information can be inserted in more than one IMF, using spread spectrum modulation, allowing hence the increase of the insertion capacity. The imperceptibility of the inserted data is ensured by the use of a psychoacoustical model. The blind extraction of the watermark signal, from the received watermarked audio, consists in the separation of the watermark from the IMFs of the received audio signal. The separation is achieved by a new proposed under-determined ISA method, here referred to as UISA. The proposed hybrid watermarking system was applied to the SQAM (Sound Quality Assessment Material) audio database (Available at http://sound.media.mit.edu/mpeg4/audio/sqam/ ) and proved to have efficient detection performances in terms of Bit Error Rate (BER) compared to a generic perceptual spread spectrum watermarking system. The perceptual quality of the watermarked audio was objectively assessed using the PEMO-Q (Tool for objective perceptual assessment of audio quality) algorithm. Also, using our technique, we can extract the different watermarks without using any information of original signal or the inserted watermark. Experimental results exhibit that the transparency and high robustness of the watermarked audio can be achieved simultaneously with a substantial increase of the amount of information transmitted. A reliability of 1.8 10Â?Â?Â?4 (against 1.5 10Â?Â?Â?2 for the generic system), for a bit rate of 400 bits/s, can be achieved when the channel is not disturbed.

Performance of the audio signals transmission over wireless networks with the channel interleaving considerations

This article studies a vital issue in wireless communications, which is the transmission of audio signals over wireless networks. It presents a novel interleaver scheme for protection against error bursts and reduction the packet loss of the audio signals. The proposed technique in the article is the chaotic interleaver; it is based on chaotic Baker map. It is used as a randomizing data tool to improve the quality of the audio over the mobile communications channels. A comparison study between the proposed chaotic interleaving scheme and the traditional block and convolutional interleaving schemes for audio transmission over uncorrelated and correlated fading channels is presented. The simulation results show the superiority of the proposed chaotic interleaving scheme over the traditional schemes. The simulation results also reveal that the proposed chaotic interleaver improves the quality of the received audio signal. It improves the amount of the throughput over the wireless link through the packet loss reduction.

Spectral Magnitude Adjustment for MCLT-Based Acoustic Data Transmission

Acoustic data transmission is a technique which embeds data in a sound wave imperceptibly and detects it at a receiver. The data are embedded in an original audio signal and transmitted through the air by playing back the data-embedded audio using a loudspeaker. At the receiver, the data are extracted from the received audio signal captured by a microphone. In our previous work, we proposed an acoustic data transmission system designed based on phase modification of the modulated complex lapped transform (MCLT) coefficients. In this paper, we propose the spectral magnitude adjustment (SMA) technique which not only enhances the quality of the data-embedded audio signal but also improves the transmission performance of the system.

Noise Suppression Circuit For Mobile Phone

A noise suppression circuit and method for a mobile phone is disclosed that includes a codec for decoding a received audio signal; a speaker for reproducing the audio signal; and a low-pass filter, which includes: a resistor; and a varactor and a capacitor connected in parallel to ground. Characteristics of the low-pass filter are adjusted according to usage conditions, thereby effectively suppressing a white noise and popup noise during an ongoing call.

Bit rate reduction in audio encoders by exploiting inharmonicity effects and auditory temporal masking

The present invention relates to a method for encoding an audio signal. In a first embodiment a model relating to temporal masking of sound provided to a human ear is provided. A temporal masking index is determined in dependence upon a received audio signal and the model using a forward and a backward masking function. Using a psychoacoustic model a masking threshold is determined in dependence upon the temporal masking index. Finally, the audio signal is encoded in dependence upon the masking threshold. The method has been implemented using the MPEG- 1 psychoacoustic model 2. Semiformal listening test showed that using the method for encoding an audio signal according to the present invention the subjective high quality of the decoded compressed sounds has been maintained while the bit rate was reduced by approximately 10%. In a second embodiment, the inharmonic structure of audio signals is modeled and incorporated into the MPEG-1 psychoacoustic model 2. In the model, the relationship between the spectral components of the input audio signal is considered and an inharmonicity index is defined and incorporated into the MPEG-1 psychoacoustic model 2. Informal listening tests have shown that the bit rate required for transparent coding of inharmonic (multi-tonal) audio material can be reduced by 10% if the modified psychoacoustic model 2 is used in the MPEG 1 Layer II encoder.

Multi-coil coupling system for hearing aid applications

A hearing improvement device using a multi-coil coupling system and methods for operating such a device are disclosed. An embodiment of the present invention may use an array microphone to provide highly directional reception. The received audio signal may be filtered, amplified, and converted into a magnetic field for coupling to the telecoil in a conventional hearing aid. Multiple transmit inductors may be used to effectively couple to both in-the-ear and behind-the-ear type hearing aids, and an additional embodiment is disclosed which may be used with an earphone, for users not requiring a hearing aid.

Volume regulating and monitoring system

A volume controlling system and method for regulating the output volume of a headset in order to protect the hearing ability of a user. The system includes a volume sensor/controller for monitoring the volume output of the headset by adjusting the volume to be within a predetermined threshold. A warning indicator indicates to the user or a person monitoring the listening user that the volume is set too high. The method include the steps of comparing the received audio signal to the predetermined volume threshold and adjusting the volume accordingly. The method also includes the steps of notifying that the volume has been set at too high a level by providing a warning signal, such as an audible or visual alarm.

Apparatus and method for outputting audio signal of laptop computer coupled with docking station

An apparatus and a method for outputting an audio signal of a laptop computer to a first speaker in the laptop computer and a to second speaker in a docking station are provided. The apparatus includes a first controller implemented in the laptop computer for filtering a high frequency band component and a low frequency band component from the audio signal, for mixing the filtered signals, and for outputting the mixed signal to the first speaker when the laptop computer is not coupled with the docking station, and otherwise, filtering only a high frequency band component from the audio signal and outputting the filtered signal to the first speaker. A second controller is implemented in the docking station for receiving the audio signal from the laptop computer, for filtering a low frequency band component from the received audio signal, and for outputting the filtered signal to the second speaker when the laptop computer is coupled with the docking station. When the laptop computer is coupled with the docking station, the audio signal generated in the laptop computer is divided into high and low frequency band signals, and each divided signal is output to different speakers according to the characteristics of the divided signals. Thus, efficiency in use of the speakers and sound quality are improved.

Second-order adaptive differential microphone array

A second-order adaptive differential microphone array (ADMA) has two first-order elements (e.g., 802 and 804 of FIG. 8), each configured to convert a received audio signal into an electrical signal. The ADMA also has (i) two delay nodes (e.g., 806 and 808 ) configured to delay the electrical signals from the first-order elements and (ii) two subtraction nodes (e.g., 810 and 812 ) configured to generate forward-facing and backward-facing cardioid signals based on differences between the electrical signals and the delayed electrical signals. The ADMA also has (i) an amplifier (e.g., 814 ) configured to amplify the backward-facing cardioid signal by a gain parameter; (ii) a third subtraction node (e.g., 816 ) configured to generate a difference signal based on a difference between the forward-facing cardioid signal and the amplified backward-facing cardioid signal; and (iii) a lowpass filter (e.g., 818 ) configured to filter the difference signal from the third subtraction node to generate the output signal for the second-order ADMA. The gain parameter for the amplifier can be adaptively adjusted to move a null in the back half plane of the ADMA to track a moving noise source. In a subband implementation, a different gain parameter can be adaptively adjusted to move a different null in the back half plane to track a different moving noise source for each different frequency subband.

Personal computer having a sound card

Telephone Device for the Deaf (TDD transitive) for a computing system and method of encoding and decoding TDD signals are described. The apparatus includes a sound card enabled to transmit and receive TDD coded Frequency Keying modulated audio signals. In extracting characters from a received audio signal, the sound card is used to sample the received audio signal. In extracting data bits from the received audio signal, groups of samples representing portions of the received signal are correlated against two frequencies.

Acoustical considerations for an automotive hands-free mobile terminal

The harsh acoustical environment of an automotive interior yields significant difficulty in controlling acoustic echo and noise during ‘‘hands-free’’ wireless phone calls. In addition to having multiple closely coupled reflection paths, the user (talker) is often positioned between the loudspeaker and microphone path. This can cause frequent changes in the acoustic path which necessitates fast and accurate adaptation of the acoustic echo canceller and/or non-linear controller. Further, the frequency response between the talker and the microphone is altered due to cabin resonances and modes; it is also strongly affected by their positions. Additional acoustical considerations for automotive hands-free implementations are the high levels and non-stationary statistics of background noise, high amplification, and possible resultant distortion of the received audio signal, and non-linear network echo. These impairments yield significant challenges in controlling the acoustic echo and noise in full-duplex, partial-duplex, and even half-duplex systems. In this work, impulse response, frequency response, and SNR measurements are presented and analyzed to demonstrate the critical differences between a traditional desktop speakerphone and an automotive hands-free mobile terminal.

Acoustic echo cancellation in an integrated audio and telecommunication system

Robust acoustic echo cancellation in a single-channel integrated audio and intercom system is provided by adapting echo cancellation filters in accordance with an optimized autocorrelation matrix. This is carried out in a modified FIR LMS filter in which the adaptation regressor signal is formed by a combination of one or more of the received voice signal, the received audio signal, and/or a random noise signal. In a SISO system, it is usually desirable to shelter the regressor signal from tonal components such as voice signals. In a multiple-channel system, it is usually desirable to shelter regressor signals for a particular channel from acoustic components that are likely to be correlated to acoustic components in other parts of the system and are likely to affect that particular channel. In multi-channel applications, it is therefore usually desirable to shelter regressor signals from audio signals.

Audio amplifier arrangement

The disclosed audio amplifier arrangement includes a first amplifier stage for influencing the signal strength of the received audio signal for at least part of the frequency spectrum of an audio signal received through an input. An output amplifier stage amplifies the signal influenced by the first amplifier stage. A detection present in the output amplifier stage detects overloads of the output amplifier stage (3). A control circuit causes, in response to the detection signal, an adjustment to be made of the influence caused by the first amplifier stage if the overload signalled by the detection signal transgresses a certain limit. This limit depends on the magnitude of the adjustment already carried out, so that the degree of overload corresponding to the limit increases when the extent of the adjustment increases.

Addressed audio and video signal transmission through optical fibers

Describes an addressed audio and video signal transmission system in which audio and video signals together with their address consisting of up to 8 frequencies can simultaneously be sent from the sender to the designated receivers through the optical fibers. The received audio signal covered the frequency range of 10 Hz to 20 kHz with a distortion factor of less than 0.5% and with an SNR of 60 dB (62 dB without addresses). The received video signal covered the frequency range of 100 Hz to 4.2 MHz with a differential gain (DG) of less than 1.0% and with a differential phase (DP) of less than 2.5 degrees.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>