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A Spectrogram Model for Enhanced Source Localization and Noise-Robust ASR

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Binaural recording

Binaural recording is a method of recording sound that uses two microphones, arranged with the intent to create a 3-D stereo sound sensation for the listener of actually being in the room with the performers or instruments. This effect is often created using a technique known as dummy head recording, wherein a mannequin head is fitted with a microphone in each ear. Binaural recording is intended for replay using headphones and will not translate properly over stereo speakers.

Speech synthesis

Speech 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.

Economic sector

One classical breakdown of economic activity distinguishes three sectors: Primary: involves the retrieval and production of raw-material commodities, such as corn, coal, wood or iron. Miners, farmers and fishermen are all workers in the primary sector. Secondary: involves the transformation of raw or intermediate materials into goods, as in steel into cars, or textiles into clothing. Builders and dressmakers work in the secondary sector. Tertiary: involves the supplying of services to consumers and businesses, such as babysitting, cinemas or banking.

Signal-to-noise ratio

Signal-to-noise ratio (SNR or S/N) is a measure used in science and engineering that compares the level of a desired signal to the level of background noise. SNR is defined as the ratio of signal power to noise power, often expressed in decibels. A ratio higher than 1:1 (greater than 0 dB) indicates more signal than noise. SNR is an important parameter that affects the performance and quality of systems that process or transmit signals, such as communication systems, audio systems, radar systems, imaging systems, and data acquisition systems.

Deep learning

Deep 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.

Speech recognition

Speech recognition is an interdisciplinary subfield of computer science and computational linguistics that develops methodologies and technologies that enable the recognition and translation of spoken language into text by computers. It is also known as automatic speech recognition (ASR), computer speech recognition or speech to text (STT). It incorporates knowledge and research in the computer science, linguistics and computer engineering fields. The reverse process is speech synthesis.

Expectation–maximization algorithm

In statistics, an expectation–maximization (EM) algorithm is an iterative method to find (local) maximum likelihood or maximum a posteriori (MAP) estimates of parameters in statistical models, where the model depends on unobserved latent variables. The EM iteration alternates between performing an expectation (E) step, which creates a function for the expectation of the log-likelihood evaluated using the current estimate for the parameters, and a maximization (M) step, which computes parameters maximizing the expected log-likelihood found on the E step.

Language model

A language model is a probabilistic model of a natural language that can generate probabilities of a series of words, based on text corpora in one or multiple languages it was trained on. Large language models, as their most advanced form, are a combination of feedforward neural networks and transformers. They have superseded recurrent neural network-based models, which had previously superseded the pure statistical models, such as word n-gram language model.

Three-sector model

The three-sector model in economics divides economies into three sectors of activity: extraction of raw materials (primary), manufacturing (secondary), and service industries which exist to facilitate the transport, distribution and sale of goods produced in the secondary sector (tertiary). The model was developed by Allan Fisher, Colin Clark, and Jean Fourastié in the first half of the 20th century, and is a representation of an industrial economy. It has been criticised as inappropriate as a representation of the economy in the 21st century.

Statistical model

A statistical model is a mathematical model that embodies a set of statistical assumptions concerning the generation of sample data (and similar data from a larger population). A statistical model represents, often in considerably idealized form, the data-generating process. When referring specifically to probabilities, the corresponding term is probabilistic model. A statistical model is usually specified as a mathematical relationship between one or more random variables and other non-random variables.