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.
Artificial neural networkArtificial neural networks (ANNs, also shortened to neural networks (NNs) or neural nets) are a branch of machine learning models that are built using principles of neuronal organization discovered by connectionism in the biological neural networks constituting animal brains. An ANN is based on a collection of connected units or nodes called artificial neurons, which loosely model the neurons in a biological brain. Each connection, like the synapses in a biological brain, can transmit a signal to other neurons.
Recurrent neural networkA recurrent neural network (RNN) is one of the two broad types of artificial neural network, characterized by direction of the flow of information between its layers. In contrast to uni-directional feedforward neural network, it is a bi-directional artificial neural network, meaning that it allows the output from some nodes to affect subsequent input to the same nodes. Their ability to use internal state (memory) to process arbitrary sequences of inputs makes them applicable to tasks such as unsegmented, connected handwriting recognition or speech recognition.
Types of artificial neural networksThere are many types of artificial neural networks (ANN). Artificial neural networks are computational models inspired by biological neural networks, and are used to approximate functions that are generally unknown. Particularly, they are inspired by the behaviour of neurons and the electrical signals they convey between input (such as from the eyes or nerve endings in the hand), processing, and output from the brain (such as reacting to light, touch, or heat). The way neurons semantically communicate is an area of ongoing research.
Sound recording and reproductionSound recording and reproduction is the electrical, mechanical, electronic, or digital inscription and re-creation of sound waves, such as spoken voice, singing, instrumental music, or sound effects. The two main classes of sound recording technology are analog recording and digital recording. Sound recording is the transcription of invisible vibrations in air onto a storage medium such as a phonograph disc. The process is reversed in sound reproduction, and the variations stored on the medium are transformed back into sound waves.
Audio engineerAn audio engineer (also known as a sound engineer or recording engineer) helps to produce a recording or a live performance, balancing and adjusting sound sources using equalization, dynamics processing and audio effects, mixing, reproduction, and reinforcement of sound. Audio engineers work on the "technical aspect of recording—the placing of microphones, pre-amp knobs, the setting of levels. The physical recording of any project is done by an engineer... the nuts and bolts.
Reel-to-reel audio tape recordingReel-to-reel audio tape recording, also called open-reel recording, is magnetic tape audio recording in which the recording tape is spooled between reels. To prepare for use, the supply reel (or feed reel) containing the tape is placed on a spindle or hub. The end of the tape is manually pulled from the reel, threaded through mechanical guides and over a tape head assembly, and attached by friction to the hub of the second, initially empty takeup reel. Reel-to-reel systems use tape that is wide, which normally moves at .
Audio signalAn audio signal is a representation of sound, typically using either a changing level of electrical voltage for analog signals, or a series of binary numbers for digital signals. Audio signals have frequencies in the audio frequency range of roughly 20 to 20,000 Hz, which corresponds to the lower and upper limits of human hearing. Audio signals may be synthesized directly, or may originate at a transducer such as a microphone, musical instrument pickup, phonograph cartridge, or tape head.
Digital recordingIn digital recording, an audio or video signal is converted into a stream of discrete numbers representing the changes over time in air pressure for audio, or chroma and luminance values for video. This number stream is saved to a storage device. To play back a digital recording, the numbers are retrieved and converted back into their original analog audio or video forms so that they can be heard or seen.
Multitrack recordingMultitrack recording (MTR), also known as multitracking, is a method of sound recording developed in 1955 that allows for the separate recording of multiple sound sources or of sound sources recorded at different times to create a cohesive whole. Multitracking became possible in the mid-1950s when the idea of simultaneously recording different audio channels to separate discrete "tracks" on the same reel-to-reel tape was developed.
Feedforward neural networkA feedforward neural network (FNN) is one of the two broad types of artificial neural network, characterized by direction of the flow of information between its layers. Its flow is uni-directional, meaning that the information in the model flows in only one direction—forward—from the input nodes, through the hidden nodes (if any) and to the output nodes, without any cycles or loops, in contrast to recurrent neural networks, which have a bi-directional flow.
Mastering (audio)Mastering, a form of audio post production, is the process of preparing and transferring recorded audio from a source containing the final mix to a data storage device (the master), the source from which all copies will be produced (via methods such as pressing, duplication or replication). In recent years digital masters have become usual, although analog masters—such as audio tapes—are still being used by the manufacturing industry, particularly by a few engineers who specialize in analog mastering.
Deep learningDeep learning is part of a broader family of machine learning methods, which is based on artificial neural networks with representation learning. The adjective "deep" in deep learning refers to the use of multiple layers in the network. Methods used can be either supervised, semi-supervised or unsupervised.
Sentiment analysisSentiment analysis (also known as opinion mining or emotion AI) is the use of natural language processing, text analysis, computational linguistics, and biometrics to systematically identify, extract, quantify, and study affective states and subjective information. Sentiment analysis is widely applied to voice of the customer materials such as reviews and survey responses, online and social media, and healthcare materials for applications that range from marketing to customer service to clinical medicine.
History of sound recordingThe history of sound recording - which has progressed in waves, driven by the invention and commercial introduction of new technologies — can be roughly divided into four main periods: The Acoustic era (1877–1925) The Electrical era (1925–1945) The Magnetic era (1945–1975) The Digital era (1975–present) Experiments in capturing sound on a recording medium for preservation and reproduction began in earnest during the Industrial Revolution of the 1800s.
Speech synthesisSpeech synthesis is the artificial production of human speech. A computer system used for this purpose is called a speech synthesizer, and can be implemented in software or hardware products. A text-to-speech (TTS) system converts normal language text into speech; other systems render symbolic linguistic representations like phonetic transcriptions into speech. The reverse process is speech recognition. Synthesized speech can be created by concatenating pieces of recorded speech that are stored in a database.
Audio editing softwareAudio editing software is any software or computer program which allows editing and generating of audio data. Audio editing software can be implemented completely or partly as a library, as a computer application, as a web application, or as a loadable kernel module. Wave editors are digital audio editors. There are many sources of software available to perform this function. Most can edit music, apply effects and filters, and adjust stereo channels.
MIMOIn radio, multiple-input and multiple-output (MIMO) (ˈmaɪmoʊ,_ˈmiːmoʊ) is a method for multiplying the capacity of a radio link using multiple transmission and receiving antennas to exploit multipath propagation. MIMO has become an essential element of wireless communication standards including IEEE 802.11n (Wi-Fi 4), IEEE 802.11ac (Wi-Fi 5), HSPA+ (3G), WiMAX, and Long Term Evolution (LTE). More recently, MIMO has been applied to power-line communication for three-wire installations as part of the ITU G.
Spiking neural networkArtificial neural network Spiking neural networks (SNNs) are artificial neural networks that more closely mimic natural neural networks. In addition to neuronal and synaptic state, SNNs incorporate the concept of time into their operating model. The idea is that neurons in the SNN do not transmit information at each propagation cycle (as it happens with typical multi-layer perceptron networks), but rather transmit information only when a membrane potential—an intrinsic quality of the neuron related to its membrane electrical charge—reaches a specific value, called the threshold.
Digital audioDigital audio is a representation of sound recorded in, or converted into, digital form. In digital audio, the sound wave of the audio signal is typically encoded as numerical samples in a continuous sequence. For example, in CD audio, samples are taken 44,100 times per second, each with 16-bit sample depth. Digital audio is also the name for the entire technology of sound recording and reproduction using audio signals that have been encoded in digital form.
