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Audio Inpainting
Audio inpainting (also known as audio interpolation) is an audio restoration task which deals with the reconstruction of missing or corrupted portions of a digital audio signal. Inpainting techniques are employed when parts of the audio have been lost due to various factors such as transmission errors, data corruption or errors during recording. The goal of audio inpainting is to fill in the gaps (i.e., the missing portions) in the audio signal seamlessly, making the reconstructed portions indistinguishable from the original content and avoiding the introduction of audible distortions or alterations. Many techniques have been proposed to solve the audio inpainting problem and this is usually achieved by analyzing the temporal and spectral information surrounding each missing portion of the considered audio signal. Classic methods employ statistical models or digital signal processing algorithms to predict and synthesize the missing or damaged sections. Recent solutions, inste ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Audio Restoration
Audio restoration is the process of removing imperfections (such as white noise, hiss, impulse noise (audio), impulse noise, crackle, wow (recording), wow and flutter (electronics and communication), flutter, background noise, and mains hum) from Sound recording and reproduction, sound recordings. Audio restoration can be performed directly on the recording medium (for example, washing a gramophone record with a cleansing solution), or on a Digital data, digital representation of the recording using a computer (such as an Audio Interchange File Format, AIFF or WAV file). Record restoration is a particular form of audio restoration that seeks to repair the sound of damaged gramophone records. Modern audio restoration techniques are usually performed by digitizing an audio source from analog signal, analog media, such as lacquer recordings, Optical disc, optical sources and magnetic tape. Once in the digital realm, recordings can be restored and cleaned up using dedicated, standal ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Regularization (mathematics)
In mathematics, statistics, Mathematical finance, finance, and computer science, particularly in machine learning and inverse problems, regularization is a process that converts the Problem solving, answer to a problem to a simpler one. It is often used in solving ill-posed problems or to prevent overfitting. Although regularization procedures can be divided in many ways, the following delineation is particularly helpful: * Explicit regularization is regularization whenever one explicitly adds a term to the optimization problem. These terms could be Prior probability, priors, penalties, or constraints. Explicit regularization is commonly employed with ill-posed optimization problems. The regularization term, or penalty, imposes a cost on the optimization function to make the optimal solution unique. * Implicit regularization is all other forms of regularization. This includes, for example, early stopping, using a robust loss function, and discarding outliers. Implicit regularizat ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Audio Forensics
Audio forensics is the field of forensic science relating to the acquisition, analysis, and evaluation of sound recordings that may ultimately be presented as admissible evidence in a court of law or some other official venue. Audio forensic evidence may come from a criminal investigation by law enforcement or as part of an official inquiry into an accident, fraud, accusation of slander, or some other civil incident. The primary aspects of audio forensics are establishing the ''authenticity'' of audio evidence, performing Forensic audio enhancement, ''enhancement'' of audio recordings to improve speech intelligibility and the audibility of low-level sounds, and ''interpreting and documenting'' sonic evidence, such as identifying talkers, transcribing dialog, and reconstructing crime or accident scenes and timelines. Modern audio forensics makes extensive use of digital signal processing, with the former use of analog filters now being obsolete. Techniques such as adaptive filterin ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Packet Loss Concealment
Packet loss concealment (PLC) is a technique to mask the effects of packet loss in voice over IP (VoIP) communications. When the voice signal is sent as VoIP packets on an IP network, the packets may (and likely will) travel different routes. A packet therefore might arrive very late, might be corrupted, or simply might not arrive at all. One example case of the last situation could be, when a packet is rejected by a server which has a full buffer and cannot accept any more data. Other cases include network congestion resulting in significant delay. In a VoIP connection, error-control techniques such as automatic repeat request (ARQ) are usually not feasible and the receiver should be able to cope with packet loss. Packet loss concealment is the inclusion in a design of methodologies for accounting for and compensating for the loss of voice packets. PLC techniques * Zero insertion: the lost speech frames are replaced with silence. * Waveform substitution: the missing gap is recon ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Compact Disc
The compact disc (CD) is a Digital media, digital optical disc data storage format co-developed by Philips and Sony to store and play digital audio recordings. It employs the Compact Disc Digital Audio (CD-DA) standard and was capable of holding of uncompressed stereo audio. First released in Japan in October 1982, the CD was the second optical disc format to reach the market, following the larger LaserDisc (LD). In later years, the technology was adapted for computer data storage as CD-ROM and subsequently expanded into various writable and multimedia formats. , over 200 billion CDs (including audio CDs, CD-ROMs, and CD-Rs) had been sold worldwide. Standard CDs have a diameter of and typically hold up to 74 minutes of audio or approximately of data. This was later regularly extended to 80 minutes or by reducing the spacing between data tracks, with some discs unofficially reaching up to 99 minutes or which falls outside established specifications. Smaller variants, such ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Audio Forensics
Audio forensics is the field of forensic science relating to the acquisition, analysis, and evaluation of sound recordings that may ultimately be presented as admissible evidence in a court of law or some other official venue. Audio forensic evidence may come from a criminal investigation by law enforcement or as part of an official inquiry into an accident, fraud, accusation of slander, or some other civil incident. The primary aspects of audio forensics are establishing the ''authenticity'' of audio evidence, performing Forensic audio enhancement, ''enhancement'' of audio recordings to improve speech intelligibility and the audibility of low-level sounds, and ''interpreting and documenting'' sonic evidence, such as identifying talkers, transcribing dialog, and reconstructing crime or accident scenes and timelines. Modern audio forensics makes extensive use of digital signal processing, with the former use of analog filters now being obsolete. Techniques such as adaptive filterin ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Training, Validation, And Test Data Sets
In machine learning, a common task is the study and construction of algorithms that can learn from and make predictions on data. Such algorithms function by making data-driven predictions or decisions, through building a mathematical model from input data. These input data used to build the model are usually divided into multiple data sets. In particular, three data sets are commonly used in different stages of the creation of the model: training, validation, and test sets. The model is initially fit on a training data set, which is a set of examples used to fit the parameters (e.g. weights of connections between neurons in artificial neural networks) of the model. The model (e.g. a naive Bayes classifier) is trained on the training data set using a supervised learning method, for example using optimization methods such as gradient descent or stochastic gradient descent. In practice, the training data set often consists of pairs of an input vector (or scalar) and the corresp ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Diffusion Model
In machine learning, diffusion models, also known as diffusion-based generative models or score-based generative models, are a class of latent variable model, latent variable generative model, generative models. A diffusion model consists of two major components: the forward diffusion process, and the reverse sampling process. The goal of diffusion models is to learn a diffusion process for a given dataset, such that the process can generate new elements that are distributed similarly as the original dataset. A diffusion model models data as generated by a diffusion process, whereby a new datum performs a Wiener process, random walk with drift through the space of all possible data. A trained diffusion model can be sampled in many ways, with different efficiency and quality. There are various equivalent formalisms, including Markov chains, denoising diffusion probabilistic models, noise conditioned score networks, and stochastic differential equations. They are typically trained ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Minimax
Minimax (sometimes Minmax, MM or saddle point) is a decision rule used in artificial intelligence, decision theory, combinatorial game theory, statistics, and philosophy for ''minimizing'' the possible loss function, loss for a Worst-case scenario, worst case (''max''imum loss) scenario. When dealing with gains, it is referred to as "maximin" – to maximize the minimum gain. Originally formulated for several-player zero-sum game theory, covering both the cases where players take alternate moves and those where they make simultaneous moves, it has also been extended to more complex games and to general decision-making in the presence of uncertainty. Game theory In general games The maximin value is the highest value that the player can be sure to get without knowing the actions of the other players; equivalently, it is the lowest value the other players can force the player to receive when they know the player's action. Its formal definition is: :\underline = \max_ \min_ W ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Neural Network
A neural network is a group of interconnected units called neurons that send signals to one another. Neurons can be either biological cells or signal pathways. While individual neurons are simple, many of them together in a network can perform complex tasks. There are two main types of neural networks. *In neuroscience, a '' biological neural network'' is a physical structure found in brains and complex nervous systems – a population of nerve cells connected by synapses. *In machine learning, an '' artificial neural network'' is a mathematical model used to approximate nonlinear functions. Artificial neural networks are used to solve artificial intelligence problems. In biology In the context of biology, a neural network is a population of biological neurons chemically connected to each other by synapses. A given neuron can be connected to hundreds of thousands of synapses. Each neuron sends and receives electrochemical signals called action potentials to its conne ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Generative Adversarial Network
A generative adversarial network (GAN) is a class of machine learning frameworks and a prominent framework for approaching generative artificial intelligence. The concept was initially developed by Ian Goodfellow and his colleagues in June 2014. In a GAN, two neural networks compete with each other in the form of a zero-sum game, where one agent's gain is another agent's loss. Given a training set, this technique learns to generate new data with the same statistics as the training set. For example, a GAN trained on photographs can generate new photographs that look at least superficially authentic to human observers, having many realistic characteristics. Though originally proposed as a form of generative model for unsupervised learning, GANs have also proved useful for semi-supervised learning, fully supervised learning, and reinforcement learning. The core idea of a GAN is based on the "indirect" training through the discriminator, another neural network that can tell ho ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Generative Model
In statistical classification, two main approaches are called the generative approach and the discriminative approach. These compute classifiers by different approaches, differing in the degree of statistical modelling. Terminology is inconsistent, but three major types can be distinguished: # A generative model is a statistical model of the joint probability distribution P(X, Y) on a given observable variable ''X'' and target variable ''Y'';: "Generative classifiers learn a model of the joint probability, p(x, y), of the inputs ''x'' and the label ''y'', and make their predictions by using Bayes rules to calculate p(y\mid x), and then picking the most likely label ''y''. A generative model can be used to "generate" random instances ( outcomes) of an observation ''x''. # A discriminative model is a model of the conditional probability P(Y\mid X = x) of the target ''Y'', given an observation ''x''. It can be used to "discriminate" the value of the target variable ''Y'', given an ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
