This document discusses dynamic processors and their use in audio production. Dynamic processors include compressors, limiters, expanders, and noise gates. They are used to control the dynamic range of a recording by making the volume more consistent or emphasizing certain parts. Key components of dynamic processors include the threshold, ratio, attack, and release settings. The threshold sets the input level at which processing begins, while the ratio determines how much the gain is reduced. The attack and release control how quickly processing engages and disengages.
equalization in digital audio production graduation level education it is useful as the reference for bachelor of art students who choose mass communication as the main stream. a presentation from st.joseph's college
The document discusses different types of jitter that can occur in electronic signals. It defines random jitter as stochastic variations caused by thermal and shot noise that typically follow a Gaussian distribution. Deterministic jitter is defined as predictable variations that can be non-Gaussian, including duty cycle distortion, inter-symbol interference, and periodic jitter caused by external factors like power supply noise. The document explains how both random and deterministic jitter components combine to impact the overall jitter observed in real-world signals and timing measurements.
Digital filters can remove unwanted noise from signals or extract useful frequency components. They operate by sampling an analog signal, processing the digital values, and converting back to analog. Finite impulse response (FIR) filters use weighted sums of past inputs for outputs and are inherently stable without feedback. Infinite impulse response (IIR) filters use feedback, with outputs and next states determined by inputs and past outputs. Common filters include moving average filters and filters that introduce gain, delay, or differences between signal values. Design involves selecting coefficients for desired frequency responses. Stability depends on pole locations within the unit circle. Digital filters find applications in communications, audio, imaging, and other areas.
This document provides an overview of digital filter design. It introduces finite impulse response (FIR) and infinite impulse response (IIR) filters. FIR filters are designed using window techniques like rectangular, Hamming, and Kaiser windows. IIR filters are designed using approximation methods like Butterworth, Chebyshev I, and Chebyshev II. MATLAB code is provided to design low pass, high pass, and other filters using different window and approximation techniques. Pros and cons of FIR and IIR filters are discussed along with references.
Delta modulation is an analog-to-digital conversion technique used to transfer data. It works by comparing an input signal to a reference signal and encoding the difference into a digital bitstream. A delta modulation system consists of a modulator that converts an analog signal to digital, and a demodulator that converts the digital signal back to analog. Delta modulation is simpler than pulse code modulation but can achieve high signal-to-noise ratios and variable bandwidth. However, it is limited by slope overload when signals change rapidly.
This document discusses dynamic processors and their use in audio production. Dynamic processors include compressors, limiters, expanders, and noise gates. They are used to control the dynamic range of a recording by making the volume more consistent or emphasizing certain parts. Key components of dynamic processors include the threshold, ratio, attack, and release settings. The threshold sets the input level at which processing begins, while the ratio determines how much the gain is reduced. The attack and release control how quickly processing engages and disengages.
equalization in digital audio production graduation level education it is useful as the reference for bachelor of art students who choose mass communication as the main stream. a presentation from st.joseph's college
The document discusses different types of jitter that can occur in electronic signals. It defines random jitter as stochastic variations caused by thermal and shot noise that typically follow a Gaussian distribution. Deterministic jitter is defined as predictable variations that can be non-Gaussian, including duty cycle distortion, inter-symbol interference, and periodic jitter caused by external factors like power supply noise. The document explains how both random and deterministic jitter components combine to impact the overall jitter observed in real-world signals and timing measurements.
Digital filters can remove unwanted noise from signals or extract useful frequency components. They operate by sampling an analog signal, processing the digital values, and converting back to analog. Finite impulse response (FIR) filters use weighted sums of past inputs for outputs and are inherently stable without feedback. Infinite impulse response (IIR) filters use feedback, with outputs and next states determined by inputs and past outputs. Common filters include moving average filters and filters that introduce gain, delay, or differences between signal values. Design involves selecting coefficients for desired frequency responses. Stability depends on pole locations within the unit circle. Digital filters find applications in communications, audio, imaging, and other areas.
This document provides an overview of digital filter design. It introduces finite impulse response (FIR) and infinite impulse response (IIR) filters. FIR filters are designed using window techniques like rectangular, Hamming, and Kaiser windows. IIR filters are designed using approximation methods like Butterworth, Chebyshev I, and Chebyshev II. MATLAB code is provided to design low pass, high pass, and other filters using different window and approximation techniques. Pros and cons of FIR and IIR filters are discussed along with references.
Delta modulation is an analog-to-digital conversion technique used to transfer data. It works by comparing an input signal to a reference signal and encoding the difference into a digital bitstream. A delta modulation system consists of a modulator that converts an analog signal to digital, and a demodulator that converts the digital signal back to analog. Delta modulation is simpler than pulse code modulation but can achieve high signal-to-noise ratios and variable bandwidth. However, it is limited by slope overload when signals change rapidly.
Delta modulation, delta-sigma modulation, and adaptive delta modulation are analog-to-digital conversion techniques. Delta modulation samples an input signal and outputs a single bit indicating the sign of the difference between samples. It has higher SNR than other techniques but can suffer from slope overload and granular noise. Delta-sigma modulation places an integrator between the source and quantizer, reducing quantization error and more closely matching the original wave. Adaptive delta modulation uses a variable step size to better handle rapidly changing slopes and slow changes, improving quality.
This document discusses the design of IIR and FIR filters. IIR (Infinite Impulse Response) filters are analog filters that use feedback and have non-linear phase responses. Common IIR design methods are impulse invariant, bilinear transformation, and approximation of derivatives. FIR (Finite Impulse Response) filters are digital filters with no feedback and linear phase responses. FIR filters are designed using windowing methods like rectangular, Hamming, and Kaiser windows which concentrate the filter response around the desired frequencies. IIR filters require less computation but FIR filters are required where linear phase response is needed such as data transmission and speech processing.
It is sometimes desirable to have circuits capable of selectively filtering one frequency or range of frequencies out of a mix of different frequencies in a circuit. A circuit designed to perform this frequency selection is called a filter circuit, or simply a filter. A common need for filter circuits is in high-performance stereo systems, where certain ranges of audio frequencies need to be amplified or suppressed for best sound quality and power efficiency. You may be familiar with equalizers, which allow the amplitudes of several frequency ranges to be adjusted to suit the listener's taste and acoustic properties of the listening area. You may also be familiar with crossover networks, which block certain ranges of frequencies from reaching speakers. A tweeter (high-frequency speaker) is inefficient at reproducing low-frequency signals such as drum beats, so a crossover circuit is connected between the tweeter and the stereo's output terminals to block low-frequency signals, only passing high-frequency signals to the speaker's connection terminals. This gives better audio system efficiency and thus better performance. Both equalizers and crossover networks are examples of filters, designed to accomplish filtering of certain frequencies.
Implementation and comparison of Low pass filters in Frequency domainZara Tariq
This document summarizes a presentation on implementing and comparing low pass filters in the frequency domain. It introduces low pass filters and their use in smoothing images by reducing high frequencies. It then compares ideal, Butterworth, and Gaussian low pass filters. The document demonstrates implementing each filter type in MATLAB on sample images and analyzing the results. Code examples are provided for applying the different low pass filters using 2D fast Fourier transforms.
This document discusses digital signal processing and the design of finite impulse response (FIR) filters using the window method. It begins with an introduction to FIR filters, noting their advantages over infinite impulse response (IIR) filters such as being easily designed with linear phase and being unconditionally stable. The document then covers FIR filter design concepts like phase delay, linear phase response, and filter specifications. It presents the window method approach to FIR filter coefficient calculation and discusses filter design considerations like coefficient calculation methods and filter structure selection.
Design of Filter Circuits using MATLAB, Multisim, and ExcelDavid Sandy
The purpose of this project was to design crossover active filter circuits, in order to drive music through three different types of speakers. So, high frequencies would be sent through a Tweeter speaker, low frequencies would be sent through a Woofer speaker, and middle frequencies would be sent through a Midbass driver speaker. Three circuits were created to drive these speakers. Multisim, MATLAB, and Excel, were all used in the design process in order to create the filter circuits correctly.
This document provides an overview of digital filters and focuses on finite impulse response (FIR) filters. It defines digital filtering and compares it to analog filtering. It describes different types of digital filters including FIR filters and explains how to design, implement and characterize FIR filters. Key aspects of FIR filters are that they have a finite impulse response, linear phase, and are always stable. Design techniques like windowing methods and Parks-McClellan optimization are covered.
The document discusses digital filter design. It begins by defining digital filters and their purposes, which include signal separation and distortion removal. It then covers the main types of digital filters - finite impulse response (FIR) and infinite impulse response (IIR) filters. FIR filters are implemented non-recursively without feedback, while IIR filters use recursion and feedback. The document outlines FIR filter design methods like windowing and discusses applications of digital filters such as noise suppression, frequency enhancement, and interference removal. In conclusion, digital filters can have linear phase response and are not affected by environmental factors like heat.
Pulse modulation schemes aim to transfer an analog signal over an analog channel as a two-level signal by modulating a pulse wave. Some schemes also allow digital transfer of the analog signal with a fixed bit rate. Pulse modulation includes analog-over-analog methods like PAM, PWM, and PPM as well as analog-over-digital methods like PCM, DPCM, ADPCM, DM, and delta-sigma modulation. Sampling is the reduction of a continuous signal to a discrete signal by taking values at points in time. The Nyquist-Shannon sampling theorem states that a bandlimited signal can be perfectly reconstructed from samples if the sampling rate is at least twice the highest frequency in the signal.
1) Pulse amplitude modulation (PAM) is used to digitize analog voice signals by sampling the amplitude of the voice waves at discrete time intervals.
2) For faithful reproduction of the original analog signal, the sampling rate must be at least twice the highest frequency component of the voice signal, as per the Nyquist criterion.
3) If the sampling rate is lower than the Nyquist rate, aliasing or foldover distortion will occur, distorting the reconstructed signal.
Digital Signal Processing-Digital FiltersNelson Anand
This document discusses digital signal processing using digital filters in MATLAB. It begins by introducing signals and their analog and digital processing. It then covers key digital signal processing tasks like filtering, transforms, and convolution. It describes different filter types including FIR and IIR, and filter design methods. MATLAB sessions are included to demonstrate filtering and filter design. The overall document provides a conceptual overview of digital filters and digital signal processing.
Pulse code modulation (PCM) is an analog-to-digital conversion technique used to represent sampled analog signals as digital data. PCM involves sampling the analog signal at regular intervals, quantizing the amplitude of the signal at each point to a few discrete levels, and coding it as digital data. The sampling rate must be greater than twice the highest frequency of the analog signal as per the Nyquist sampling theorem. PCM was invented in 1937 but was not widely adopted until the 1940s. It became the standard method for digital telephony due to its robustness and ability to efficiently regenerate and transmit signals.
A filter is an electrical network that transmits signals within a specified frequency range called the pass band, and suppresses signals in the stop band, separated by the cut-off frequency. Digital filters are used to eliminate noise and extract signals of interest, implemented using software rather than RLC components. Digital filters are FIR (finite impulse response) or IIR (infinite impulse response) depending on the number of sample points used. An ideal filter would transmit signals in the pass band without attenuation and completely suppress the stop band, but ideal filters cannot be realized. IIR filter design first develops an analog IIR filter, then converts it to digital using methods like impulse invariant, approximation of derivatives, or bilinear transformation.
1) The document discusses various topics related to digital communication including sampling theory, analog to digital conversion, pulse code modulation, quantization, coding, and time division multiplexing.
2) In analog to digital conversion, an analog signal is sampled, quantized by assigning it to discrete amplitude levels, and coded by mapping each level to a binary sequence.
3) The Nyquist sampling theorem states that a signal must be sampled at a rate at least twice its highest frequency to avoid aliasing when reconstructing the original signal.
The document describes the design and realization of a digital FIR filter using the Dolph-Chebyshev window method. It discusses how Dolph-Chebyshev windows can be used to generate FIR filter coefficients that minimize side lobes, improving efficiency. The key steps are:
1) FIR filters are designed using the window method, which maps an analog filter to digital by applying a window function.
2) Dolph-Chebyshev windows use Chebyshev polynomials to generate coefficients that minimize side lobes compared to other windows.
3) The FIR filter coefficients are calculated from the Dolph-Chebyshev window function.
4) The FIR filter is then realized by computing the output as a weighted
Quantization is the process of mapping continuous range of values to a finite set of values. It involves rounding samples to the nearest quantization level, changing infinite precision values to finite precision. For a given input signal sampled at 8 samples per second ranging from -1 to 1, quantization with 2 bits would result in 4 quantization levels spaced 0.5 units apart. The quantized values and errors can be calculated, with the errors assumed to be uniformly distributed between -0.25 and 0.25.
The document discusses pulse code modulation (PCM) for encoding analog waveforms into digital signals. It covers:
1. PCM involves sampling, quantizing, and encoding analog signals. Sampling makes the signal discrete in time. Quantizing makes it discrete in amplitude by rounding to discrete levels. Encoding maps quantized values to binary code words.
2. Quantization introduces distortion but sampling noise can be eliminated if the Nyquist criterion is met. Uniform quantizers are optimal for uniformly distributed inputs.
3. A practical PCM system was designed for telephone systems using 8-bit samples at 8 kHz to encode voice signals between 300-3400 Hz, producing a 64 kbps digital signal. The bandwidth
The document summarizes key aspects of designing digital IIR filters. IIR filters are computationally efficient due to feedback but can become unstable if coefficients deviate from values. The design process involves 5 steps: specifying the filter, calculating coefficients, selecting a structure, simulating, and implementing. Common filter types include Butterworth (maximally flat), Chebyshev (equiripple in pass/stopband), and Elliptic (equiripple in both). Frequency transformations can derive high-pass, band-pass, and band-stop filters from a low-pass prototype. Digital design involves transforming an analog prototype using impulse invariance or bilinear transformation.
Growing Your Audience: Reaching Kids Online with Digital Museum Educational R...Darren Milligan
Museums’ traditional education outreach philosophies center on direct contact with teachers: one teacher will impact many students. The success of this model, however, relies heavily on the teachers' discovery of your content and their ability to manipulate it into their district or state-controlled curricula. As technology lowers the barriers to direct outreach, the opportunity exists for museums to transform their formal educational resources into informal digital educational experiences for kids directly, in the school or at home.
Smithsonian in Your Classroom (SIYC), reaches more than 80,000 schools twice a year. The session presents a case study illustrating a kid-centric reinvention of the SIYC publication. The process of creating both print and interactive game/simulation will be discussed. Participants will see that the challenge is not one of digitization of the existing lesson plans, but the transformation of the educational content from a teacher-led classroom group activity to a more personalized self-directed online.
This short story is about a woman named Nana Mouskouri from Athens, Greece. She is referred to as the White Rose of Athens. The story provides her name and location but does not include any other details about her or the plot.
Delta modulation, delta-sigma modulation, and adaptive delta modulation are analog-to-digital conversion techniques. Delta modulation samples an input signal and outputs a single bit indicating the sign of the difference between samples. It has higher SNR than other techniques but can suffer from slope overload and granular noise. Delta-sigma modulation places an integrator between the source and quantizer, reducing quantization error and more closely matching the original wave. Adaptive delta modulation uses a variable step size to better handle rapidly changing slopes and slow changes, improving quality.
This document discusses the design of IIR and FIR filters. IIR (Infinite Impulse Response) filters are analog filters that use feedback and have non-linear phase responses. Common IIR design methods are impulse invariant, bilinear transformation, and approximation of derivatives. FIR (Finite Impulse Response) filters are digital filters with no feedback and linear phase responses. FIR filters are designed using windowing methods like rectangular, Hamming, and Kaiser windows which concentrate the filter response around the desired frequencies. IIR filters require less computation but FIR filters are required where linear phase response is needed such as data transmission and speech processing.
It is sometimes desirable to have circuits capable of selectively filtering one frequency or range of frequencies out of a mix of different frequencies in a circuit. A circuit designed to perform this frequency selection is called a filter circuit, or simply a filter. A common need for filter circuits is in high-performance stereo systems, where certain ranges of audio frequencies need to be amplified or suppressed for best sound quality and power efficiency. You may be familiar with equalizers, which allow the amplitudes of several frequency ranges to be adjusted to suit the listener's taste and acoustic properties of the listening area. You may also be familiar with crossover networks, which block certain ranges of frequencies from reaching speakers. A tweeter (high-frequency speaker) is inefficient at reproducing low-frequency signals such as drum beats, so a crossover circuit is connected between the tweeter and the stereo's output terminals to block low-frequency signals, only passing high-frequency signals to the speaker's connection terminals. This gives better audio system efficiency and thus better performance. Both equalizers and crossover networks are examples of filters, designed to accomplish filtering of certain frequencies.
Implementation and comparison of Low pass filters in Frequency domainZara Tariq
This document summarizes a presentation on implementing and comparing low pass filters in the frequency domain. It introduces low pass filters and their use in smoothing images by reducing high frequencies. It then compares ideal, Butterworth, and Gaussian low pass filters. The document demonstrates implementing each filter type in MATLAB on sample images and analyzing the results. Code examples are provided for applying the different low pass filters using 2D fast Fourier transforms.
This document discusses digital signal processing and the design of finite impulse response (FIR) filters using the window method. It begins with an introduction to FIR filters, noting their advantages over infinite impulse response (IIR) filters such as being easily designed with linear phase and being unconditionally stable. The document then covers FIR filter design concepts like phase delay, linear phase response, and filter specifications. It presents the window method approach to FIR filter coefficient calculation and discusses filter design considerations like coefficient calculation methods and filter structure selection.
Design of Filter Circuits using MATLAB, Multisim, and ExcelDavid Sandy
The purpose of this project was to design crossover active filter circuits, in order to drive music through three different types of speakers. So, high frequencies would be sent through a Tweeter speaker, low frequencies would be sent through a Woofer speaker, and middle frequencies would be sent through a Midbass driver speaker. Three circuits were created to drive these speakers. Multisim, MATLAB, and Excel, were all used in the design process in order to create the filter circuits correctly.
This document provides an overview of digital filters and focuses on finite impulse response (FIR) filters. It defines digital filtering and compares it to analog filtering. It describes different types of digital filters including FIR filters and explains how to design, implement and characterize FIR filters. Key aspects of FIR filters are that they have a finite impulse response, linear phase, and are always stable. Design techniques like windowing methods and Parks-McClellan optimization are covered.
The document discusses digital filter design. It begins by defining digital filters and their purposes, which include signal separation and distortion removal. It then covers the main types of digital filters - finite impulse response (FIR) and infinite impulse response (IIR) filters. FIR filters are implemented non-recursively without feedback, while IIR filters use recursion and feedback. The document outlines FIR filter design methods like windowing and discusses applications of digital filters such as noise suppression, frequency enhancement, and interference removal. In conclusion, digital filters can have linear phase response and are not affected by environmental factors like heat.
Pulse modulation schemes aim to transfer an analog signal over an analog channel as a two-level signal by modulating a pulse wave. Some schemes also allow digital transfer of the analog signal with a fixed bit rate. Pulse modulation includes analog-over-analog methods like PAM, PWM, and PPM as well as analog-over-digital methods like PCM, DPCM, ADPCM, DM, and delta-sigma modulation. Sampling is the reduction of a continuous signal to a discrete signal by taking values at points in time. The Nyquist-Shannon sampling theorem states that a bandlimited signal can be perfectly reconstructed from samples if the sampling rate is at least twice the highest frequency in the signal.
1) Pulse amplitude modulation (PAM) is used to digitize analog voice signals by sampling the amplitude of the voice waves at discrete time intervals.
2) For faithful reproduction of the original analog signal, the sampling rate must be at least twice the highest frequency component of the voice signal, as per the Nyquist criterion.
3) If the sampling rate is lower than the Nyquist rate, aliasing or foldover distortion will occur, distorting the reconstructed signal.
Digital Signal Processing-Digital FiltersNelson Anand
This document discusses digital signal processing using digital filters in MATLAB. It begins by introducing signals and their analog and digital processing. It then covers key digital signal processing tasks like filtering, transforms, and convolution. It describes different filter types including FIR and IIR, and filter design methods. MATLAB sessions are included to demonstrate filtering and filter design. The overall document provides a conceptual overview of digital filters and digital signal processing.
Pulse code modulation (PCM) is an analog-to-digital conversion technique used to represent sampled analog signals as digital data. PCM involves sampling the analog signal at regular intervals, quantizing the amplitude of the signal at each point to a few discrete levels, and coding it as digital data. The sampling rate must be greater than twice the highest frequency of the analog signal as per the Nyquist sampling theorem. PCM was invented in 1937 but was not widely adopted until the 1940s. It became the standard method for digital telephony due to its robustness and ability to efficiently regenerate and transmit signals.
A filter is an electrical network that transmits signals within a specified frequency range called the pass band, and suppresses signals in the stop band, separated by the cut-off frequency. Digital filters are used to eliminate noise and extract signals of interest, implemented using software rather than RLC components. Digital filters are FIR (finite impulse response) or IIR (infinite impulse response) depending on the number of sample points used. An ideal filter would transmit signals in the pass band without attenuation and completely suppress the stop band, but ideal filters cannot be realized. IIR filter design first develops an analog IIR filter, then converts it to digital using methods like impulse invariant, approximation of derivatives, or bilinear transformation.
1) The document discusses various topics related to digital communication including sampling theory, analog to digital conversion, pulse code modulation, quantization, coding, and time division multiplexing.
2) In analog to digital conversion, an analog signal is sampled, quantized by assigning it to discrete amplitude levels, and coded by mapping each level to a binary sequence.
3) The Nyquist sampling theorem states that a signal must be sampled at a rate at least twice its highest frequency to avoid aliasing when reconstructing the original signal.
The document describes the design and realization of a digital FIR filter using the Dolph-Chebyshev window method. It discusses how Dolph-Chebyshev windows can be used to generate FIR filter coefficients that minimize side lobes, improving efficiency. The key steps are:
1) FIR filters are designed using the window method, which maps an analog filter to digital by applying a window function.
2) Dolph-Chebyshev windows use Chebyshev polynomials to generate coefficients that minimize side lobes compared to other windows.
3) The FIR filter coefficients are calculated from the Dolph-Chebyshev window function.
4) The FIR filter is then realized by computing the output as a weighted
Quantization is the process of mapping continuous range of values to a finite set of values. It involves rounding samples to the nearest quantization level, changing infinite precision values to finite precision. For a given input signal sampled at 8 samples per second ranging from -1 to 1, quantization with 2 bits would result in 4 quantization levels spaced 0.5 units apart. The quantized values and errors can be calculated, with the errors assumed to be uniformly distributed between -0.25 and 0.25.
The document discusses pulse code modulation (PCM) for encoding analog waveforms into digital signals. It covers:
1. PCM involves sampling, quantizing, and encoding analog signals. Sampling makes the signal discrete in time. Quantizing makes it discrete in amplitude by rounding to discrete levels. Encoding maps quantized values to binary code words.
2. Quantization introduces distortion but sampling noise can be eliminated if the Nyquist criterion is met. Uniform quantizers are optimal for uniformly distributed inputs.
3. A practical PCM system was designed for telephone systems using 8-bit samples at 8 kHz to encode voice signals between 300-3400 Hz, producing a 64 kbps digital signal. The bandwidth
The document summarizes key aspects of designing digital IIR filters. IIR filters are computationally efficient due to feedback but can become unstable if coefficients deviate from values. The design process involves 5 steps: specifying the filter, calculating coefficients, selecting a structure, simulating, and implementing. Common filter types include Butterworth (maximally flat), Chebyshev (equiripple in pass/stopband), and Elliptic (equiripple in both). Frequency transformations can derive high-pass, band-pass, and band-stop filters from a low-pass prototype. Digital design involves transforming an analog prototype using impulse invariance or bilinear transformation.
Growing Your Audience: Reaching Kids Online with Digital Museum Educational R...Darren Milligan
Museums’ traditional education outreach philosophies center on direct contact with teachers: one teacher will impact many students. The success of this model, however, relies heavily on the teachers' discovery of your content and their ability to manipulate it into their district or state-controlled curricula. As technology lowers the barriers to direct outreach, the opportunity exists for museums to transform their formal educational resources into informal digital educational experiences for kids directly, in the school or at home.
Smithsonian in Your Classroom (SIYC), reaches more than 80,000 schools twice a year. The session presents a case study illustrating a kid-centric reinvention of the SIYC publication. The process of creating both print and interactive game/simulation will be discussed. Participants will see that the challenge is not one of digitization of the existing lesson plans, but the transformation of the educational content from a teacher-led classroom group activity to a more personalized self-directed online.
This short story is about a woman named Nana Mouskouri from Athens, Greece. She is referred to as the White Rose of Athens. The story provides her name and location but does not include any other details about her or the plot.
The document discusses how the Smithsonian Institution is working to give teachers more control over educational resources by developing the Smithsonian Learning Lab digital platform. It provides an overview of the Smithsonian, details research conducted on how educators use digital resources, and describes the Learning Lab which provides over 1 million digitized objects and lessons to teachers. The goal is to better understand teacher needs and empower them to create customized lessons and share resources through the platform.
SME2: Social Media Excellence x Social Media ExpertiseRichard Binhammer
SME2 is a consultancy that assesses social media skills, workflows, and staffing levels to help clients optimize their social media programs. It uses a series of tools to audit social media competencies and interview teams. SME2 identifies strengths, gaps in skills and processes, and provides recommendations to improve capabilities, leverage existing staff, and close competency gaps through training. The assessment covers 33 skills across content, project management, social media expertise, and leadership. SME2 helps clients maximize the effectiveness of in-house and agency partnerships to better achieve social media goals.
A Synthesis Review of Key Lessons in Programs Relating to Oceans and FisheriesThe Rockefeller Foundation
This synthesis was designed to provide an evidence base on the success factors in small-scale coastal fisheries management in developing countries and, in turn, to assist the Rockefeller Foundation in developing its strategy for its Oceans and Fisheries Initiative. In doing so, it identifies and describes some 20 key factors believed to influence success in small-scale coastal fisheries management.
The report was completed via a rapid review of key sources of knowledge from formal published literature, institutional literature, key informants and Internet searches. The focus was on key success factors in achieving a balance of social, economic and ecological benefits from the management of small-scale coastal fisheries.
TheCrowdCafe: Global Crowdinvesting Industry Presentation Jonathan Sandlund
This document discusses the emerging investment crowdfunding industry and the impact of the JOBS Act. It summarizes data on global crowdfunding deals and regulations allowing general solicitation. The JOBS Act represents the first major change to securities law in over 70 years by allowing internet platforms to facilitate private capital raising, creating a more efficient process. Cultural trends also support crowdfunding as individuals seek more meaningful investment opportunities.
We offer personalized training and tech support services tailored for Mac users, including business startup packages that provide marketing and branding projects as well as client communications support.
El documento presenta varios eventos culturales y espectáculos que tendrán lugar en Saucepolis y Zaragoza entre el 12 y el 18 de abril, incluyendo una adaptación teatral de la obra Glengarry Glen Ross en el Teatro Principal, el II Festival Gastronómico de Zaragoza en el Auditorio, una exposición sobre juegos y deportes tradicionales en el Centro de Historia, y un concierto de la banda Shuarma en un bar local. También incluye los horarios de apertura de varios museos e iglesias princip
La Facultad de Ciencias de la Educación, Humanas y Tecnologías aspira a ser reconocida a nivel nacional e internacional por liderar procesos educativos innovadores y por ofrecer una diversificación de carreras que incorporen nuevos paradigmas para la formación integral de docentes. Su misión es formar, capacitar y profesionalizar a maestros de todos los niveles del sistema educativo ecuatoriano de acuerdo a tendencias pedagógicas contemporáneas para brindar una educación de calidad a todos los sectores sociales
El documento presenta los horarios y actividades navideñas en la ciudad de Zaragoza durante las fechas del 23 de diciembre al 29 de diciembre. Se destacan los horarios de lugares de interés como la Basílica de El Pilar, la Seo, la Plaza del Pilar y museos. También se anuncia el Festival Internacional de Magos Callejeros del 26 al 29 de diciembre y las funciones de cuentos clásicos de Teatro Arbolé del 20 de diciembre al 5 de enero. Por último, se incluye el
This document proposes an integrated hardware-software approach to flexible transactional memory. It includes:
1) Hardware support for conflict detection and data versioning to accelerate STMs while allowing flexible software policies.
2) A decoupled design where hardware provides alerts and isolation, while software controls conflict resolution and recovery.
3) The approach eliminates copying overhead and reduces validation costs, improving STM performance for various workloads.
Photo of the Day from Outdoor Photographer Magazinemaditabalnco
This document contains descriptions of 34 nature photographs taken in various locations around the world, including national parks and other scenic areas in the United States, Canada, Europe, Africa, and Asia. The photographs depict autumn scenery, wildlife, landscapes, seascapes, and other natural scenes. Locations range from California to Florida, Utah to Maine, Austria to Spain, and Alaska to Africa.
Todas las semanas en Saucépolis publicamos un resumen con algunos de los acontecimientos de interés cultural, de ocio o turístico que más pueden interesar a los zaragozanos y a la gente que nos visita: es nuestra gaceta a la que llamamos "Saucépolis News". Este es un breve resumen de los acontecimientos turísticos y de ocio en Zaragoza esta semana:Disney on Ice
El espectáculo sobre hielo mas popular para toda la familia trae este año un montaje con las princesas favoritas de los mas pequeños. Con Campanilla como maestra de ceremonias, Ariel, yasmin, Bella, Blancanieves, Cenicienta, Mulán o la Bella durmiente realizarán piruetas de patinaje artístico sobre el hielo del Principe Felipe.
Pabellon Principe Felipe
Jueves 11 de Marzo 19:00
Viernes 12 de Marzo 19:30
Sábado y Domingo 13 y 14 de Marzo 12:00, 16:00 y 19:30
Horarios de interés
Basílica de
El Pilar
6:45-21:30
Todos los días
La Seo
Lunes cerrado
Sabado 10:00-12:30
15:00-18:00
Domingo 10:00-11:30
Resto de días 9:00-13:30
16:00-18:30
Museos arqueológicos
Lunes cerrado
Domingos 10:00-13:30
Resto de días 10:00-20:30
Aljafería
10:00-20:30
Todos los días
Domingos entrada gratuita
Rodin en las calles de Zaragoza
En concreto en la calle Alfonso I y en la Plaza del Pilar. Los Burgueses de Calais y el célebre Pensador constituyen esta muestra que forma parte del programa Arte en la Calle. Es una ocasión única de contemplar estas obras, cedidas por el museo Rodin de Paris, que permanece cerrado por obras. Una estampa irrepetible, una oportunidad singular de ver las obras de, según muchos, el escultor mas importante desde Miguel Angel en las calles de esta ciudad.
Calle Alfonso I
Trayectos: Danza en las calles de Zaragoza
El ya tradicional festival de danza en las calles de Zaragoza celebra este sábado una edición especial en interiores. Escenarios espectáculares como el Hall del Teatro Principal, el museo del Foro o el Teatro Romano serán testigos de este espectáculo de danza contemporanea
Sabado 13 de Marzo a partir 18:00
Selecta: Del Greco a Picasso, última semana.
Última oprtunidad de contemplar la imponente colección pictórica del Banco Santander. 62 obras maestras que abarcan desde el renacimiento a nuestros días. El Greco, Zurbarán, Sorolla, Picasso, Tapies o Chillida entre otros. Todo ello en el marco incomparable de la antigua facultad de medicina de la universidad de Zaragoza. Un precioso edificio de finales del siglo XIX de Ricardo Magdalena. Simplemente imprescindible.
Paraninfo de la Universidad de Zaragoza
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The document provides an overview of audio compression, including:
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1. Hi. My name is Jan Zurcher.
I’m a singer-songwriter living in a
small town called Friday Harbor, San
Juan Island, Washington
This lesson is for Week 4 of the
Introduction to Music Production
Course at Coursera.org.
The topic I have chosen to look at
this week is…
2.
3. Dynamic Processors are used to control the dynamic range of a recording
• to enhance the highs and/or lows of your tracks to make the recording
more exciting or to emphasize a particular part of a song;
• to reduce the highs and/or the lows;
• to make the amplitude more consistent;
• to eliminate unwanted noise; or,
• to adjust the transients (loud sounds at the start of a sound)
Use of Dynamic Processors
4. Dynamic Range
Dynamic range is the difference between the loudest and the quietest sounds. You could say
that the dynamic range is represented by the red line shown above.
However, if you tried to analyse the signal above by finding the average volume, you would end
up with an average of 0 because the wave spends as much time about the 0 mark as below it.
For this reason, dynamic processors often use the RMS (Root Mean Squared) measured in dB
with 0 as one point of reference and -60 dB as another to determine the average loudness of a
signal.
When you record a sound in your DAW, you get a wave form that looks something like this:
5. Dynamic Range
A larger segment of the audio recording looks like this:
If this wave form were converted into an RMS curve, the result would look something
like this:
Here you can see that the (uncompressed) range is from -54.0 dB to +4.5 dB (or a
dynamic range of 58.5 dB).
6. Dynamic Range
A common way that you see the dynamic range represented in your DAW is on the fader
meter where the scale ranges from +6 dB to around -60 dB (which is basically zero
sound).
For another segment of the above audio signal captured in a screen shot, the loudest
point was +1.8 dB and the lowest was -16 dB (or a dynamic range of 17.8 dB as depicted
by the red arrow).
Dynamic Range
7. Common Types of Dynamic Processors
Compressor
Expander
Limiter Noise Gate
Dynamic range can be changed using any of a number of devices called Dynamic Processors
including (but not limited to): Compressors, Expanders, Limiters and Noise Gates.
Shown here are screen shots of some of the built-in Dynamic Processors in Logic Express 9.0.
8. Key Components of Dynamic Processors
Although each of these dynamic processors creates changes in dynamic range
a little differently, they have some common components and work on some
common principles.
First, each of these is basically divided into two sections – one section analyses
the input signal to find the shape of the sound envelop (the RMS discussed
above). This is called the “side chain section” (or key section).
The other section contains the volume fader that is used to make adjustments
in the audio level – either up or down as needed.
There are also a number of other factors that can be adjusted to create the
type of dynamic range that you want.
9. Common Elements of Dynamic Processors
These other adjustment factors include :
Threshold: The volume level at which the dynamic processor begins to
affect the signal
Ratio: How much the input volume is changed as a proportion of
output level over the threshold
Attack: How fast the processor begins to alter the signal once the
threshold is reached.
Release How fast the processor stops altering the signal once the
threshold is crossed back into the acceptable range
Let’s take a closer look at what each of these terms mean when it comes to
processing an audio signal. In each case, the examples are assuming the
use of a downward compressor.
10. Input Level (dB)
In this example, the threshold is set at -20 dB. This threshold can be adjusted to
whatever input level you need for the effect you want. A threshold set at -60 dB will
affect the entire signal. A threshold of 0 dB would not affect any of the signal.
-60 -50 -40 -30 -20 -10
Threshold
For downward compression, when the input level of an audio signal goes above a
specified point (called the threshold), gain reduction begins.
Threshold Level – Downward Compression
11. Input Level (dB)
-60 -50 -40 -30 -20 -10
-10
-30
-20
-40
-50
-60
Once you have told the compressor the input level at which it should start processing
the signal (that is, the threshold level), you next need to let it know how much you
want it to reduce the gain of the output.
This is determined by the ratio. If you set the ratio to 1:1 (output:input), even if the
threshold is crossed, there will be no gain reduction because the ratio of 1:1 indicates
that the output level should equal the input level.
OutputLevel(dB)
Compression Ratio
12. Input Level (dB)
-60 -50 -40 -30 -20 -10
If you select a ratio of 2:1, after the threshold is crossed, if the input level is 20 dB
over the threshold level, the output level will be reduced so that it is only 10 dB
over the threshold level.
-10
-30
-20
-40
-50
-60
OutputLevel(dB)
Reduction in output gain
13. Input Level (dB)
-60 -50 -40 -30 -20 -10
-10
-30
-20
-40
-50
-60
OutputLevel(dB)
Input Level (dB)
-60 -50 -40 -30 -20 -10
-10
-30
-20
-40
-50
-60
OutputLevel(dB)
The threshold level and the ratio work together to establish how much reduction
in the gain of the output signal there will be.
In this example, with a -30 dB
threshold and a 2:1 ratio, the gain
reduction is 15 dB.
In this example, with a -50 dB
threshold and a 2:1 ratio, the gain
reduction is 25 dB.
Reduction
in gain Reduction
in gain
14. Input Level (dB)
-60 -50 -40 -30 -20 -10
-10
-30
-20
-40
-50
-60
OutputLevel(dB)
Input Level (dB)
-60 -50 -40 -30 -20 -10
-10
-30
-20
-40
-50
-60
OutputLevel(dB)
For downward compression, as the ratio gets larger, the amount of gain reduction
increases until finally, at a ratio of Infinity:1, the output level of the signal will be
the same as the threshold level.
At high ratios, a compressor acts like a limiter – limiting the level of the output
signal to the threshold level.
In this example, with a -40 dB
threshold and a 4:1 ratio, the gain
reduction is 30 dB.
In this example, with a -50 dB
threshold and a 40:1 ratio, the gain
reduction is 39 dB.
Reduction
in gain Reduction
in gain
15. Once you have told the device the threshold level and the relative
amount that you want the output signal to be reduced, the next step
is to tell it how quickly you want it to react when it detects that the
threshold has been crossed.
This is known as the Attack Phase which is how long it takes the
processor to decrease the gain down to the level established by the
ratio.
This attack time is measured in milliseconds (ms) and can be set to be
very fast ( < 1 ms) or relatively slow ( > 100 ms).
Compressors in Logic Express 9.0 allow adjustments in attack from 0
ms to 200 ms in 0.5 ms intervals.
Attack Time
16. Fast attack results in the gain being reduced almost instantly as soon as the
threshold is crossed. This may not be what you want because that transient
information at the beginning of a sound adds significantly to the character
of that sound. Reducing it right away can lead to a dull sound.
On the other hand, if the attack time is set too slow, the dynamic processor
may act too late and be less effective.
Input Level
Output Level
Time (ms)
dB
Input Level
Output Level
Time (ms)
dB
Attack PhaseAttack
Phase
Fast Attack Slow Attack
Threshold Threshold
17. Release Time
The final common function found in dynamic processors covered in
this presentation is the Release Phase.
Just as you have to tell the device how fast to reduce the gain to the
required level once the threshold is reached, you also need to tell it
how fast to return to the input level once the threshold is crossed in
the opposite direction.
The Release Time is measured in milliseconds (ms) and can be set to
be fast ( < 5 ms) or relatively slow ( > 5,000 ms).
Compressors in Logic Express 9.0 allow adjustments in release from 5
ms to 5000 ms.
18. A fast release time means the signal returns to match the input level almost
immediately after the threshold is crossed. You might want to use a faster
release time if you want to make dynamic differences stand out.
On the other hand, if the release time is set to a larger number of
milliseconds, the dynamic differences in the signal will be more smoothed
out – that is a gradual return to the input level.
Input Level
Output Level
Time (ms)
dB
Input Level
Output Level
Time (ms)
dB
Release PhaseRelease Phase
Fast Release Slow Release
Threshold Threshold
19. Dynamic Processors - Summary
This concludes this brief overview of the concept of Dynamic
Processors and the four common elements that these types of
processors have in common – that is threshold, ratio, attack and
release.
Although the details of the explanations were provided in the
context of how these work in a compressor, these common
elements operate in similar ways in other common types of
Dynamic Compressors including Limiters, Expanders and Noise
Gates.