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Modeling the Biologically-Inspired Audio Storing Management Process (BiASMP) to Prevent Duplication of Digital Audio File in Personal Device

Modeling the Biologically-Inspired Audio Storing Management Process (BiASMP) to Prevent Duplication of Digital Audio File in Personal Device

learning functions supporting the recognition function for giving assistance to user to store only the desired file. Other than the biologically-inspired elements, there are several elements of digital audio concept adapted for the development of BiASMP based on the requirements and issues suggested by [17] and [18]. The explanation of the elements of BiASMP is described more in detail by [11]. Based on Fig. 3., to support the recognition and learning process, a matching condition is made for the prevention action and control action for assisting user to add a digital music record into their personal device. This matching classification is entitled as CCL matching condition (See Fig. 4. in next page). The full descriptions of how BiASMP executes are explained in detail in the simulations and exemplar settings section. To further describe how BiASMP executes the CCL function, an algorithm is formulated such as below:
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Comparative study of digital audio steganography techniques

Comparative study of digital audio steganography techniques

digital audio steganography techniques and approaches is presented. In an attempt to reveal their capabili- ties in ensuring secure communications, we discussed their strengthes and weaknesses. Also, a differentiation between the reviewed techniques based on the intended applications has been highlighted. Thus, while temporal domain techniques, in general, aim to maximize the hid- ing capacity, transform domain methods exploit the mask- ing properties in order to make the noise generated by embedded data imperceptible. On the other side, encoded domain methods strive to ensure the integrity of hid- den data against challenging environment such as real time applications. To better estimate the robustness of the presented techniques, a classification based on their occurrence in the voice encoder is given. A comparison as well as a performance evaluation (i.e., imperceptibil- ity and steganalysis) for the reviewed techniques have been also presented. This study showed that the frequency domain is preferred over the temporal domain and music signals are better covers for data hiding in terms of capac- ity, imperceptibility and undetectability. From our point of view, the diversity and large number of existing audio steganography techniques expand application possibili- ties. The advantage on using one technique over another one depends on the application constraints in use and its requirement for hiding capacity, embedded data security level and encountered attacks resistance.
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Investigation of digital audio manipulation methods

Investigation of digital audio manipulation methods

Truncation of a digital waveform occurs when it is amplified such that some sample values end up exceeding the full scale deflection value (0dB-FS) which results in those samples being set to that full scale deflection value (this is also commonly known as clipping). The effect of this on audio is to produce a harsh sounding distortion, the amount of distortion being proportional to the amount of clipped samples. Although it is ideal to avoid this situation from occurring in the first place, it is becoming increas- ingly common that digital audio music files are being sold and distributed with such distortion included. Modern digital audio manipulation software packages often include a facility for removing clipping, however details on the techniques used are usually not readily available (more so in the case of commercial software).
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Watermarking Based Digital Audio Data Authentication

Watermarking Based Digital Audio Data Authentication

Digital watermarking has become an accepted technology for enabling multimedia protection schemes. While most efforts con- centrate on user authentication, recently interest in data authentication to ensure data integrity has been increasing. Existing concepts address mainly image data. Depending on the necessary security level and the sensitivity to detect changes in the media, we differentiate between fragile, semifragile, and content-fragile watermarking approaches for media authentication. Furthermore, invertible watermarking schemes exist while each bit change can be recognized by the watermark which can be extracted and the original data can be reproduced for high-security applications. Later approaches can be extended with cryptographic approaches like digital signatures. As we see from the literature, only few audio approaches exist and the audio domain requires additional strategies for time flow protection and resynchronization. To allow different security levels, we have to identify relevant audio features that can be used to determine content manipulations. Furthermore, in the field of invertible schemes, there are a bunch of publications for image and video data but no approaches for digital audio to ensure data authentication for high-security appli- cations. In this paper, we introduce and evaluate two watermarking algorithms for digital audio data, addressing content integrity protection. In our first approach, we discuss possible features for a content-fragile watermarking scheme to allow several postpro- duction modifications. The second approach is designed for high-security applications to detect each bit change and reconstruct the original audio by introducing an invertible audio watermarking concept. Based on the invertible audio scheme, we combine digital signature schemes and digital watermarking to provide a public verifiable data authentication and a reproduction of the original, protected with a secret key.
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Research on Network Transmission and Exchange Technology of Digital Audio

Research on Network Transmission and Exchange Technology of Digital Audio

Q-lan is a pure digital audio network developed by QSC Company in the United States, which is based on gigabit network processing technology. It consists of core processor and interface message processor. It is connected with a gigabit network in the middle. Its network delay is 0.667 milliseconds (5.33 milliseconds of CobraNet), maximum network throughput is 10 hops (7 hops of CobraNet), maximum input and output capacity is 512×512 channels (32×32 channels of CobraNet). Q-lan considers the security and hot backup of the system at the beginning of the system design. Each device of Q-lan has two network interfaces, which can backup each other. There is no master-slave division between the two networks. When one network fails, it is switched to another network for use. Even if the original network is restored, it is no longer automatically switched back, which can avoid the unstable problem caused by repeated switching in the case of bad network contact [14].
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An Authentication Method for Digital Audio Using a Discrete Wavelet Transform

An Authentication Method for Digital Audio Using a Discrete Wavelet Transform

Recently, several digital watermarking techniques have been proposed for hiding data in the frequency do- main of audio files in order to protect their copyrights. In general, there is a tradeoff between the quality of watermarked audio and the tolerance of watermarks to signal processing methods, such as compression. In previous research, we simultaneously improved the performance of both by developing a multipurpose opti- mization problem for deciding the positions of watermarks in the frequency domain of audio data and ob- taining a near-optimum solution to the problem. This solution was obtained using a wavelet transform and a genetic algorithm. However, obtaining the near-optimum solution was very time consuming. To overcome this issue essentially, we have developed an authentication method for digital audio using a discrete wavelet transform. In contrast to digital watermarking, no additional information is inserted into the original audio by the proposed method, and the audio is authenticated using features extracted by the wavelet transform and characteristic coding in the proposed method. Accordingly, one can always use copyright-protected original audio. The experimental results show that the method has high tolerance of authentication to all types of MP3, AAC, and WMA compression. In addition, the processing time of the method is acceptable for every- day use.
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A Survey of Digital Audio Watermarking
          Techniques

A Survey of Digital Audio Watermarking Techniques

Yan yang et al.[1] in 2009 gave a novel audio watermarking algorithm. This Algorithm uses DCT transform. The digital audio signal after subjection is transformed using DCT transform. Simultaneously, binary image is reduced dimensionally and passed through pseudo-random compositor. The converted image is embedded into the transformed audio signal whereby which Inverse DCT algorithm is applied and watermarked audio signal is obtained. In the extraction process, the audio signal is decomposed and extraction is performed on the 3 rd AC coefficient of the signal and after recovering the watermarked, it is subjected to the pseudo-random back tracking to obtain the actual 2-d image. The similarity b/w the recovered and the actual image is compared and along with that various attacks like filtering, Additive white Gaussian noise are applied to check the robustness of the algorithm. This blind audio watermarking technique given in this respective paper is robust against most of the attacks.
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Distortion-Free 1-Bit PWM Coding for Digital Audio Signals

Distortion-Free 1-Bit PWM Coding for Digital Audio Signals

Although uniformly sampled pulse width modulation (UPWM) represents a very efficient digital audio coding scheme for digital- to-analog conversion and full-digital amplification, it suffers from strong harmonic distortions, as opposed to benign non- harmonic artifacts present in analog PWM (naturally sampled PWM, NPWM). Complete elimination of these distortions usually requires excessive oversampling of the source PCM audio signal, which results to impractical realizations of digital PWM systems. In this paper, a description of digital PWM distortion generation mechanism is given and a novel principle for their minimization is proposed, based on a process having some similarity to the dithering principle employed in multibit signal quantization. This conditioning signal is termed “jither” and it can be applied either in the PCM amplitude or the PWM time domain. It is shown that the proposed method achieves significant decrement of the harmonic distortions, rendering digital PWM performance equivalent to that of source PCM audio, for mild oversampling (e.g., × 4) resulting to typical PWM clock rates of 90 MHz.
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Spread Spectrum Based Digital Audio Watermarking

Spread Spectrum Based Digital Audio Watermarking

Methodology. This paper deals with a model based on psychoacoustic auditory components (mimics the human hearing system) using direct sequence spread spectrum. It focuses on the continuous frequency masking that occurs during a hearing process which is responsible for the production of the final masking threshold which leads to the formation and shaping of the audio watermark which is hard to catch for the human ear. Also, segmentation of the signal occurs in order to overcome the issue of large lengths of the audio signal which are hard to process. Fast Fourier transform is used to convert original signal to frequency domain signal. The masking threshold is decided using spread energy per critical band. The result of the frequency domain is further converted to the time domain. This is the initial processing of the frames of the original data. Embedding happens using both the psychoacoustic model and the DSSS. After deciding the masking threshold for the particular frame of audio with the help of psychoacoustic model with frame size 2048 and FFT of 2048 points, PN sequence is generated which used to modulate the FFT watermark bits. Using this the shaping of the watermark signal is done for imperceptibility. After this watermark is embedded into the actual signal in time domain. The extraction of the watermark and its detection occurs as follows: Linear prediction filtering and linear sequence analysis are both done simultaneously after which the left audio signal is forwarded to the matched filtering procedure where the PN sequence is applied and output of this is the required extracted watermark.
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Non-intrusive identification of speech codecs in digital audio signals

Non-intrusive identification of speech codecs in digital audio signals

Speech coding has long been an important research area within the telecommunica- tions field, with efforts beginning as early as the 1930’s at Bell Telephone Laboratories. During World War II, interest in speech coding began to grow, as it allowed for more efficient representation of voice data over encrypted channels. Throughout the 1940’s and 1950’s, most speech coding implementations were based on analog speech signals, although primitive digital representations of speech (including PCM and several of its variants) were starting to be developed during the same time. By the end of the 1950’s, the underpinnings of the important source-filter model for speech synthesis had been developed. This model was augmented with linear predictive coding (LPC) techniques in the 1960’s. In conjunction with the rise of VLSI computer systems and additional research efforts in digital signal processing, these fundamental components formed the basis for a new burst of proposed speech coders in the 1970’s and 1980’s. Research in the 1980’s and 1990’s concentrated on codecs that improved the per- ceptual quality of speech at low bitrates, including the notable Code Excited Linear Prediction (CELP) codec [25]. Finally, research in the 1990’s and 2000’s concen- trated largely on robust speech codecs for mobile wireless technologies and internet voice applications [11].
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Students using digital audio interventions to enhance their learning experience

Students using digital audio interventions to enhance their learning experience

A sample of the student volunteers at Sheffield Hallam University were interviewed at the start of the project as to why the Student Audio Notes project was attractive to them. Within a few weeks of the students embarking on the project at University of Sheffield the students took part in focus groups to identify and highlight the potential learning opportunities of this approach across a range of scenarios. The following student quotes from both institution illustrate the perceived learning opportunities and benefits of student initiated audio recordings:

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Rhythmic constant pitch time stretching for digital audio

Rhythmic constant pitch time stretching for digital audio

The phase vocoder does not perform well when time stretching audio which contains short transients due to the inherent assumption that each frame contains sinusoids which are coherent across multiple frames. It also has issues with beat alignment, which suggests that further work is required perfecting the phase unwrapping part of the phase vocoder. On the other hand, the two overlap-add methods were capable of aligning the beat accents within the specified tolerance, however the two overlap-add methods proposed here each contain their own unique audible artifacts, the severity of which can be controlled by varying parameters.
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90 0500 001 C Cube Product Catalog Spring 1994 pdf

90 0500 001 C Cube Product Catalog Spring 1994 pdf

o Accepts stereo analog audio or AESIEBU digital audio as input o Encodes audio into MPEG Layer 1 or 2 format at compressed data rates of from 32 to 384 Kbits per second as per the MPEG [r]

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Data Hiding in Audio by Reserving Room in
          Advance of Encryption

Data Hiding in Audio by Reserving Room in Advance of Encryption

Steganography is a powerful instrument which increases security in data transferring and archiving. In the steganography scenario the change data is first hidden within one more object which is called “cover object”, to form “stego object” and then this new object can be transmitted or saved. It causes the existence of the convert data and even their transmissions become hidden(17). Steganography is often puzzled with cryptography because the two are similar in the way that they both are used to protect confidential information. The difference between the two is in the appearance in the processed output(8); the output of steganography operation is not apparently visible but in cryptography the output is scrambled so that it can draw attention. Steganalysis is process to detect of presence of steganography. A steganographic method of embedding textual information in an audio file is presented in this paper.(9) In this paper, first the audio file is sampled and then an appropriate bit of each alternate sample is altered to embed the textual information. ETAS model - Embedding Text in Audio Signal that embeds the text like the existing system but with encryption that gains the full advantages of cryptography. In this method for digital audio steganography where data is encrypted using (Rivert SHA ) algorithm and embedded into the host audio signal using parity technique(10). Today‟s large demand of internet applications requires data to be transmitted in a secure manner , so audio steganography is the scheme of hiding the existence of secret information by concealing it into another medium such as audio file. For a digital data hiding system to be effective and practical, it should exhibit the following characteristics(11).
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Mobile Diary & Data Management

Mobile Diary & Data Management

Advanced Audio Coding (AAC) is a standard- ized, loss compression and encoding scheme for digital audio. Designed to be the successor of the MP3 format, AAC generally achieves better sound quality than MP3 at similar bit rates [12]. AAC has been standardized by ISO and IEC, as part of the MPEG- 2 and MPEG-4 specifications [13] [14]. Part of the AAC known as High Efficiency Advanced Audio Coding (HE-AAC) which is part of MPEG-4 Audio is also adopted into digital radio standards like DAB+ and Digital Radio Mondiale, as well as mobile televi- sion standards DVB-H and ATSC-M/H. AAC supports inclusion of 48 full- bandwidth (up to 96 kHz) audio channels in one-stream plus 16 low frequency effects (LFE, limited to 120 Hz) channels, up to 16 "coupling" or dialog channels, and up to 16 da- ta streams. The quality for stereo is satisfactory to modest requirements at 96 kbit/s in joint ste- reo mode; however, hi-fi transparency demands data rates of at least 128 kbit/s (VBR). The MPEG-2 audio tests showed that AAC meets the requirements referred to as "transparent" for the ITU at 128 kbit/s for stereo, and 320 kbit/s for 5.1 audio.
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audacity manual 1 2 pdf

audacity manual 1 2 pdf

Playback of digital audio uses a Digital−to−Analog Converter (DAC). This takes the sample and sets a certain voltage on the analog outputs to recreate the signal, that the Analog−to−Digital Converter originally took to create the sample. The DAC does this as faithfully as possible and the first CD players did only that, which didn't sound good at all. Nowadays DACs use Oversampling to smooth out the audio signal. The quality of the filters in the DAC also contribute to the quality of the recreated analog audio signal. The filter is part of a multitude of stages that make up a DAC. How does audio get digitized on your computer?
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I. Unit Title: Delta Music Institute School/College or University Division: College of Arts and Sciences Unit Administrator Ms. Tricia Walker

I. Unit Title: Delta Music Institute School/College or University Division: College of Arts and Sciences Unit Administrator Ms. Tricia Walker

3. Actual Results of Evaluation: This world class recording facility, unique not only to Mississippi, but to the entire Southeast region, provides state of the art audio recording, editing, and mixing capabilities for DSU students and the community at large. A digital audio lab and an audio transfer lab are being used extensively for recording and archival projects, and ample rehearsal space is now available for performing ensembles. Newly renovated classrooms offer students a vibrant learning environment. Recording services offered to the public allow DMI students a wealth of opportunities for application of knowledge by having hands on experience in planning and executing real world projects.
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An Audio Mask Algorithm Based on Cepstrum Tansform and Wavelet Tansform

An Audio Mask Algorithm Based on Cepstrum Tansform and Wavelet Tansform

Abstract. In order to better improve audio watermark robustness and perceptual, the paper puts forward a kind of based on cepstrum transform and the wavelet transform of digital audio watermarking algorithm The algorithm will be audio signal wavelet decomposition, selection of low frequency coefficient of framing and cepstrum transform And according to the statistical mean thought, using the cepstrum domain characteristics of the energy concentration near zero, a pseudo random sequence watermark embedded in cepstrum domain experimental results show that the algorithm not only has good perceptual, but also has better robustness and can withstand attacks of common signal such as low-pass filter resampling quantitative lossy compression and sync to attack.
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Comparative Study of some Audio Watermarking Techniques based on DCT, SVD, LWT and EMD Principals

Comparative Study of some Audio Watermarking Techniques based on DCT, SVD, LWT and EMD Principals

Audio Watermarking technique involves the embedding of secret information (watermark) into a host audio signal. In order to understand the issue of protecting, monitoring & tracking the digital contents, broad understanding of the existing methods is very essential. Several watermarking schemes have been implemented for the protection of intellectual property rights of the digital audio. As Human Auditory System (HAS) is more complex, audio watermarking is more challenging than image & video watermarking. An audio signal requires less number of samples to represent the signal therefore the amount of information that can be embedded in audio signal is very less. Large numbers of algorithms were developed for secure and robust watermarking. The range starts from simple Least Significant Bit (LSB) algorithm to complex Spread Spectrum algorithms [1][4].
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Windows XP Digital Music for Dummies.pdf

Windows XP Digital Music for Dummies.pdf

I’m going to continue to push the orchestra metaphor (as a former middle school violinist, I feel I’m honor-bound to do so) in talking about the com- puter’s hard drive. If the processor is the conductor and the RAM is the play- ers, then the hard drive represents the orchestra’s repertoire, or library of songs. The hard drive is where the instructions and the data for each pro- gram and file are stored while not being used. When the processor calls for the information, it’s loaded from the hard drive into memory and run. The hard drive has two important attributes — storage space and rotations per minute, or rpm. Storage space for modern hard drives is measured in gigabytes, and most current hard drives can hold from 40GB to 200GB. Some computers can also be shipped with two hard drives to increase the amount of storage space. This is important because digital audio files tend to be rather large. Make sure you have at least a 100GB hard drive to store all of your programs and files. One problem is that manufacturers and computer scientists tend to disagree about the definition of a gigabyte. Manufacturers generally define a gigabyte as 1000MB, whereas the tech folk point out that technically 1GB of memory is slightly more that 1000MB. (This makes much more sense if you’re up on binary numbers, bits and bytes, and so on — don’t worry if you’re not.) Combine this argument with the formatting that’s required for a computer to read a hard drive, and the actual storage capacity of a hard drive is some- what less than what is advertised on the box.
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