Mel-frequency cepstrumIn sound processing, the mel-frequency cepstrum (MFC) is a representation of the short-term power spectrum of a sound, based on a linear cosine transform of a log power spectrum on a nonlinear mel scale of frequency. Mel-frequency cepstral coefficients (MFCCs) are coefficients that collectively make up an MFC. They are derived from a type of cepstral representation of the audio clip (a nonlinear "spectrum-of-a-spectrum").
NoiseNoise is unwanted sound considered unpleasant, loud, or disruptive to hearing. From a physics standpoint, there is no distinction between noise and desired sound, as both are vibrations through a medium, such as air or water. The difference arises when the brain receives and perceives a sound. Acoustic noise is any sound in the acoustic domain, either deliberate (e.g., music or speech) or unintended. In contrast, noise in electronics may not be audible to the human ear and may require instruments for detection.
Image noiseImage noise is random variation of brightness or color information in s, and is usually an aspect of electronic noise. It can be produced by the and circuitry of a or digital camera. Image noise can also originate in film grain and in the unavoidable shot noise of an ideal photon detector. Image noise is an undesirable by-product of image capture that obscures the desired information. Typically the term “image noise” is used to refer to noise in 2D images, not 3D images.
Stage monitor systemA stage monitor system is a set of performer-facing loudspeakers called monitor speakers, stage monitors, floor monitors, wedges, or foldbacks on stage during live music performances in which a sound reinforcement system is used to amplify a performance for the audience. The monitor system allows musicians to hear themselves and fellow band members clearly. The sound at popular music and rock music concerts is amplified with power amplifiers through a sound reinforcement system.
AutoencoderAn autoencoder is a type of artificial neural network used to learn efficient codings of unlabeled data (unsupervised learning). An autoencoder learns two functions: an encoding function that transforms the input data, and a decoding function that recreates the input data from the encoded representation. The autoencoder learns an efficient representation (encoding) for a set of data, typically for dimensionality reduction. Variants exist, aiming to force the learned representations to assume useful properties.
Speech codingSpeech coding is an application of data compression to digital audio signals containing speech. Speech coding uses speech-specific parameter estimation using audio signal processing techniques to model the speech signal, combined with generic data compression algorithms to represent the resulting modeled parameters in a compact bitstream. Common applications of speech coding are mobile telephony and voice over IP (VoIP).
Sound reinforcement systemA sound reinforcement system is the combination of microphones, signal processors, amplifiers, and loudspeakers in enclosures all controlled by a mixing console that makes live or pre-recorded sounds louder and may also distribute those sounds to a larger or more distant audience. In many situations, a sound reinforcement system is also used to enhance or alter the sound of the sources on the stage, typically by using electronic effects, such as reverb, as opposed to simply amplifying the sources unaltered.
Gesture recognitionGesture recognition is a topic in computer science and language technology with the goal of interpreting human gestures via mathematical algorithms. It is a subdiscipline of computer vision. Gestures can originate from any bodily motion or state, but commonly originate from the face or hand. Focuses in the field include emotion recognition from face and hand gesture recognition since they are all expressions. Users can make simple gestures to control or interact with devices without physically touching them.
DecibelThe decibel (symbol: dB) is a relative unit of measurement equal to one tenth of a bel (B). It expresses the ratio of two values of a power or root-power quantity on a logarithmic scale. Two signals whose levels differ by one decibel have a power ratio of 101/10 (approximately 1.26) or root-power ratio of 10 (approximately 1.12). The unit expresses a relative change or an absolute value. In the latter case, the numeric value expresses the ratio of a value to a fixed reference value; when used in this way, the unit symbol is often suffixed with letter codes that indicate the reference value.
Convolutional neural networkConvolutional neural network (CNN) is a regularized type of feed-forward neural network that learns feature engineering by itself via filters (or kernel) optimization. Vanishing gradients and exploding gradients, seen during backpropagation in earlier neural networks, are prevented by using regularized weights over fewer connections. For example, for each neuron in the fully-connected layer 10,000 weights would be required for processing an image sized 100 × 100 pixels.
MultilingualismMultilingualism is the use of more than one language, either by an individual speaker or by a group of speakers. It is believed that multilingual speakers outnumber monolingual speakers in the world's population. More than half of all Europeans claim to speak at least one language other than their mother tongue; but many read and write in one language. Multilingualism is advantageous for people wanting to participate in trade, globalization and cultural openness.
Affective computingAffective computing is the study and development of systems and devices that can recognize, interpret, process, and simulate human affects. It is an interdisciplinary field spanning computer science, psychology, and cognitive science. While some core ideas in the field may be traced as far back as to early philosophical inquiries into emotion, the more modern branch of computer science originated with Rosalind Picard's 1995 paper on affective computing and her book Affective Computing published by MIT Press.
