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Silence Compression
Silence compression is an audio processing technique used to effectively encode silent intervals, reducing the amount of storage or bandwidth needed to transmit audio recordings. Overview Silence can be defined as audio segments with negligible sound. Examples of silence are pauses between words or sentences in speech and pauses between notes in music. By compressing the silent intervals, the audio files become smaller and easier to handle, store, and send while still retaining the original sound quality. While techniques vary, silence compression is generally achieved through two crucial steps: detection of the silent intervals and the subsequent compression of those intervals. Applications of silence compression include telecommunications, audio streaming, voice recognition, audio archiving, and media production. Techniques 1. Trimming Trimming is a method of silence compression in which the silent intervals are removed altogether. This is done by identifying audio interva ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Telecommunications
Telecommunication, often used in its plural form or abbreviated as telecom, is the transmission of information over a distance using electronic means, typically through cables, radio waves, or other communication technologies. These means of transmission may be divided into communication channels for multiplexing, allowing for a single medium to transmit several concurrent Session (computer science), communication sessions. Long-distance technologies invented during the 20th and 21st centuries generally use electric power, and include the electrical telegraph, telegraph, telephone, television, and radio. Early telecommunication networks used metal wires as the medium for transmitting signals. These networks were used for telegraphy and telephony for many decades. In the first decade of the 20th century, a revolution in wireless communication began with breakthroughs including those made in radio communications by Guglielmo Marconi, who won the 1909 Nobel Prize in Physics. Othe ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Huffman Coding
In computer science and information theory, a Huffman code is a particular type of optimal prefix code that is commonly used for lossless data compression. The process of finding or using such a code is Huffman coding, an algorithm developed by David A. Huffman while he was a Doctor of Science, Sc.D. student at Massachusetts Institute of Technology, MIT, and published in the 1952 paper "A Method for the Construction of Minimum-Redundancy Codes". The output from Huffman's algorithm can be viewed as a variable-length code table for encoding a source symbol (such as a character in a file). The algorithm derives this table from the estimated probability or frequency of occurrence (''weight'') for each possible value of the source symbol. As in other entropy encoding methods, more common symbols are generally represented using fewer bits than less common symbols. Huffman's method can be efficiently implemented, finding a code in time linear time, linear to the number of input weigh ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Delta-sigma Modulation
Delta-sigma (ΔΣ; or sigma-delta, ΣΔ) modulation is an oversampling method for encoding signals into low bit depth digital signals at a very high sample-frequency as part of the process of delta-sigma analog-to-digital converters (ADCs) and digital-to-analog converters (DACs). Delta-sigma modulation achieves high quality by utilizing a negative feedback loop during quantization to the lower bit depth that continuously corrects quantization errors and moves quantization noise to higher frequencies well above the original signal's bandwidth. Subsequent low-pass filtering for demodulation easily removes this high frequency noise and time averages to achieve high accuracy in amplitude, which can be ultimately encoded as pulse-code modulation (PCM). Both ADCs and DACs can employ delta-sigma modulation. A delta-sigma ADC (e.g. Figure 1 top) encodes an analog signal using high-frequency delta-sigma modulation and then applies a digital filter to demodulate it t ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
High Fidelity
High fidelity (hi-fi or, rarely, HiFi) is the high-quality reproduction of sound. It is popular with audiophiles and home audio enthusiasts. Ideally, high-fidelity equipment has inaudible noise and distortion, and a flat (neutral, uncolored) frequency response within the human hearing range. High fidelity contrasts with the lower-quality " lo-fi" sound produced by inexpensive audio equipment, AM radio, or the inferior quality of sound reproduction that can be heard in recordings made until the late 1940s. History Bell Laboratories began experimenting with various recording techniques in the early 1930s. Performances by Leopold Stokowski and the Philadelphia Orchestra were recorded in 1931 and 1932 using telephone lines between the Academy of Music in Philadelphia and the Bell labs in New Jersey. Some multitrack recordings were made on optical sound film, which led to new advances used primarily by MGM (as early as 1937) and Twentieth Century Fox Film Corporation (as ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Quantization (signal Processing)
Quantization, in mathematics and digital signal processing, is the process of mapping input values from a large set (often a continuous set) to output values in a (countable) smaller set, often with a finite number of elements. Rounding and truncation are typical examples of quantization processes. Quantization is involved to some degree in nearly all digital signal processing, as the process of representing a signal in digital form ordinarily involves rounding. Quantization also forms the core of essentially all lossy compression algorithms. The difference between an input value and its quantized value (such as round-off error) is referred to as quantization error, noise or distortion. A device or algorithm function, algorithmic function that performs quantization is called a quantizer. An analog-to-digital converter is an example of a quantizer. Example For example, Rounding#Round half up, rounding a real number x to the nearest integer value forms a very basic type of q ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Derivative
In mathematics, the derivative is a fundamental tool that quantifies the sensitivity to change of a function's output with respect to its input. The derivative of a function of a single variable at a chosen input value, when it exists, is the slope of the tangent line to the graph of the function at that point. The tangent line is the best linear approximation of the function near that input value. For this reason, the derivative is often described as the instantaneous rate of change, the ratio of the instantaneous change in the dependent variable to that of the independent variable. The process of finding a derivative is called differentiation. There are multiple different notations for differentiation. '' Leibniz notation'', named after Gottfried Wilhelm Leibniz, is represented as the ratio of two differentials, whereas ''prime notation'' is written by adding a prime mark. Higher order notations represent repeated differentiation, and they are usually denoted in Leib ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Delta Modulation
Delta modulation (DM, ΔM, or Δ-modulation) is an analog-to-digital and digital-to-analog signal conversion technique used for transmission of voice information where quality is not of primary importance. DM is the simplest form of differential pulse-code modulation (DPCM) where the difference between successive samples is encoded into n-bit data streams. In delta modulation, the transmitted data are reduced to a 1-bit data stream representing either up (↗) or down (↘). Its main features are: * The analog signal is approximated with a series of segments. * Each segment of the approximated signal is compared to the preceding bits and the successive bits are determined by this comparison. * Only the change of information is sent, that is, only an increase or decrease of the signal amplitude from the previous sample is sent whereas a no-change condition causes the modulated signal to remain at the same ↗ or ↘ state of the previous sample. To achieve high signal-to-noise r ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Differential Coding
In digital communications, differential coding is a technique used to provide ''unambiguous'' signal reception when using some types of modulation. It makes transmissible data dependent on both the current and previous signal (or symbol) states. The common types of modulation that may be used with differential coding include phase-shift keying and quadrature amplitude modulation. Purposes of differential coding When data is transmitted over balanced lines, it is easy to accidentally invert polarity in the cable between the transmitter and the receiver. Similarly for BPSK. To demodulate BPSK, one needs to make a local oscillator ''synchronous'' with the remote one. This is accomplished by a carrier recovery circuit. However, the integer part of the recovered carrier is ambiguous. There are ''n'' valid but not equivalent phase shifts between the two oscillators. For BPSK, ''n'' = 2; the symbols appear inverted or not. Differential encoding prevents inversion of the signal and s ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Binary Code
A binary code represents plain text, text, instruction set, computer processor instructions, or any other data using a two-symbol system. The two-symbol system used is often "0" and "1" from the binary number, binary number system. The binary code assigns a pattern of binary digits, also known as bits, to each character, instruction, etc. For example, a binary string (computer science), string of eight bits (which is also called a byte) can represent any of 256 possible values and can, therefore, represent a wide variety of different items. In computing and telecommunications, binary codes are used for various methods of encoding data, such as character strings, into bit strings. Those methods may use fixed-width or variable-length code, variable-width strings. In a fixed-width binary code, each letter, digit, or other character is represented by a bit string of the same length; that bit string, interpreted as a binary number, is usually displayed in code tables in octal, decimal ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Variable-length Code
In coding theory, a variable-length code is a code which maps source symbols to a ''variable'' number of bits. The equivalent concept in computer science is '' bit string''. Variable-length codes can allow sources to be compressed and decompressed with ''zero'' error (lossless data compression) and still be read back symbol by symbol. With the right coding strategy, an independent and identically-distributed source may be compressed almost arbitrarily close to its entropy. This is in contrast to fixed-length coding methods, for which data compression is only possible for large blocks of data, and any compression beyond the logarithm of the total number of possibilities comes with a finite (though perhaps arbitrarily small) probability of failure. Some examples of well-known variable-length coding strategies are Huffman coding, Lempel–Ziv coding, arithmetic coding, and context-adaptive variable-length coding. Codes and their extensions The extension of a code is the m ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Entropy Coding
In information theory, an entropy coding (or entropy encoding) is any lossless data compression method that attempts to approach the lower bound declared by Shannon's source coding theorem, which states that any lossless data compression method must have an expected code length greater than or equal to the entropy of the source. More precisely, the source coding theorem states that for any source distribution, the expected code length satisfies \operatorname E_ ell(d(x))\geq \operatorname E_ \log_b(P(x))/math>, where \ell is the function specifying the number of symbols in a code word, d is the coding function, b is the number of symbols used to make output codes and P is the probability of the source symbol. An entropy coding attempts to approach this lower bound. Two of the most common entropy coding techniques are Huffman coding and arithmetic coding. If the approximate entropy characteristics of a data stream are known in advance (especially for signal compression), a simple ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Run-length Encoding
Run-length encoding (RLE) is a form of lossless data compression in which ''runs'' of data (consecutive occurrences of the same data value) are stored as a single occurrence of that data value and a count of its consecutive occurrences, rather than as the original run. As an imaginary example of the concept, when encoding an image built up from colored dots, the sequence "green green green green green green green green green" is shortened to "green x 9". This is most efficient on data that contains many such runs, for example, simple graphic images such as icons, line drawings, games, and animations. For files that do not have many runs, encoding them with RLE could increase the file size. RLE may also refer in particular to an early graphics file format supported by CompuServe for compressing black and white images, that was widely supplanted by their later Graphics Interchange Format (GIF). RLE also refers to a little-used image format in Windows 3.x that is saved with the fil ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
