Work Experience

Click for https://ccrma.stanford.edu/~jos/mdft/Constructive_Destructive_Interference.html

Noise (in signal processing) is a random signal. In the language of statistical signal processing, a noise signal is typically modeled as 'stochastic process', which is in turn defined as a sequence of random variables. — Click for https://ccrma.stanford.edu/~jos/sasp/What_Noise.html

The transfer function is defined for LTI filters as the z transform of the filter output signal, divided by the z transform of the filter input signal — Click for https://ccrma.stanford.edu/~jos/filters/Transfer_Function_Analysis.html

Click for https://ccrma.stanford.edu/~jos/sasp/Modulation_Complex_Sinusoid.html#24349

Sampling is the process of converting a continuous-time signal into a discrete-time signal. — Click for http://ccrma.stanford.edu/~jos/mdft/Sampling_Theory.html

The spectrum of a signal gives the distribution of signal energy as a function of frequency. — Click for http://ccrma.stanford.edu/~jos/mdft/Example_Applications_DFT.html

The sampling rate of a discrete-time signal is defined as the number of samples per second. Its units are thus in Hertz (Hz). Shannon's Sampling Theorem states that the original continuous-time signal can be recovered exactly from the samples if and only if the sampling rate is higher than twice the highest frequency present in the original signal. Any higher frequencies will alias to frequencies below half the sampling rate. — Click for https://ccrma.stanford.edu/~jos/mdft/Sampling_Theory.html

Click for https://www.mathworks.com/help/signal/ug/practical-introduction-to-digital-filter-design.html

Any function that is not linear is nonlinear. There are no general methods for solving nonlinear equations or nonlinear differential equations. This is why it is usually easier, although generally less accurate, to approximate a nonlinear function by a linear function if possible. — Click for http://en.wikipedia.org/wiki/Nonlinear

Click for https://ccrma.stanford.edu/~jos/sasp/Modulation_Complex_Sinusoid.html#24352

Linear Prediction is an important signal modeling tool which captures correlation information in the form of prediction coefficients. In digital audio signal processing, linear prediction computes a superior spectral envelope that emphasizes spectral peaks. — Click for http://en.wikipedia.org/wiki/Linear_prediction

Spectrum analysis of sound is analogous to decomposing white light into its component colors by means of a prism — Click for https://ccrma.stanford.edu/~jos/mdft/Example_Applications_DFT.html

The subject of Digital Signal Processing (DSP) is concerned with the computational processing of digital signals. An example digital signal is music from a compact disk (CD). An example DSP task is modifying the tone of a digital audio stream, such as boosting its bass or treble. Another DSP example is compressing digital audio into MP3 format, or decoding an MP3 stream. — Click for https://ccrma.stanford.edu/~jos/filters/Book_Series_Overview.html

Click for https://ccrma.stanford.edu/~jos/pasp/References_F0_Estimation.html

Techniques for system identification are concerned with estimating the coefficients of a filter given observations of its input and output signals. — Click for https://ccrma.stanford.edu/~jos/mdft/FIR_System_Identification.html

A filter in the audio signal processing context is any operation that accepts a signal as an input and produces a signal as an output. Most practical audio filters are linear and time invariant, in which case they can be characterized by their impulse response or their frequency response. — Click for https://ccrma.stanford.edu/~jos/filters/What_Filter.html

Bandlimited interpolation is the ideal type of interpolation for properly sampled signals. — Click for https://ccrma.stanford.edu/~jos/resample/

Reverberation is the sound created by many echoes blending together, such as you hear when you clap your hands together in an acoustically 'live' room. — Click for https://ccrma.stanford.edu/~jos/pasp/Artificial_Reverberation.html

A digital filter can be visualized as a 'black box' that accepts a sequence of numbers and emits a new sequence of numbers. In digital audio signal processing applications, such number sequences usually represent sounds. For example, digital filters are used to implement graphic equalizers and other digital audio effects. — Click for https://ccrma.stanford.edu/~jos/filters/

Click for https://ccrma.stanford.edu/~jos/pasp/Single_Reed_Instruments.html

Containing both code and data, libraries provide extended functionality to a programming environment. — Click for http://en.wikipedia.org/wiki/Library_(computer_science)

The .dsp filename suffix is used by Faust language files, and stands for Digital Signal Processing. — Click for https://faust.grame.fr/

In an object-oriented programming language, each object created during the execution of a program is an instance of a class of objects. — Click for http://en.wikipedia.org/wiki/Object-oriented_programming

A periodic signal is a signal that forever repeats itself. — Click for http://en.wikibooks.org/wiki/Signals_and_Systems/Periodic_Signals

A signal is typically a real-valued function of time. A discrete-time signal is typically a real-valued function of discrete time, and is therefore a time-ordered sequence of real numbers. — Click for http://ccrma.stanford.edu/~jos/filters/Definition_Signal.html

From Wikipedia: Acoustics is the interdisciplinary science that deals with the study of all mechanical waves in gases, liquids, and solids including topics such as vibration, sound, ultrasound and infrasound — Click for https://en.wikipedia.org/wiki/Acoustics

Work Experience

Fall 2022 through Summer 2023: Professor Emeritus of Music and by courtesy Electrical Engineering, Stanford University. Half-time teaching and research advising.

Fall 2004 through Summer 2022: Professor of Music and by courtesy Electrical Engineering, Stanford University, based at the Center for Computer Research in Music and Acoustics (CCRMA). Activities include teaching courses in signal processing and music technology, graduate student advising, and research in signal processing techniques applied to music and audio.

Fall 1994 to Fall 2004: Associate Professor of Music and by courtesy Electrical Engineering, Stanford University, at CCRMA. Activities as listed above. In addition, during this period, served as a founding consultant for Staccato Systems, Inc., and Shazam Entertainment, Ltd.

Fall 1989 to Summer 1994 (part to full time): Associate Professor (Research) at CCRMA. Activities included basic research in signal processing techniques applied to music and audio, teaching signal processing courses in support of my research program (Music 320/420/421, EE 265/266), and supervising Ph.D. research in the Computer Music Ph.D. program at CCRMA and in the Electrical Engineering Department.

Summer 1986 to Winter 1993 (part to near full time): Software engineer at NeXT Inc., responsible for signal processing software pertaining to music and audio: Designed and implemented a variable-rate transform coder for real-time compression/decompression of ``CD-quality'' audio signals on the Motorola DSP56001 signal processing chip. Managed the Sound, Music, and Signal Processing Group. Managed four outside consultants on software development projects. Co-designed the object-oriented NeXT Music Kit with David Jaffe. Designed and implemented DSP56001 software supporting the NeXT Music Kit, including the real-time DSP monitor and unit-generator modules for sound synthesis and signal processing. Wrote and supported The NeXT DSP Library. Helped support and debug the Sound/DSP Mach driver and the NeXT Sound Library.

Fall 1982 to Summer 1986 (half time): Systems Control Technology, Palo Alto CA. Projects included time-delay estimation, ARMA modeling and spectrum analysis, underwater acoustic signal processing, HF communications signal processing, and general tool development.

Summer 1980: Electromagnetic Systems Laboratories (ESL), Inc., Sunnyvale CA. Application of linear prediction to FSK demodulation.

Summer 1980: Integrated Systems Inc., Palo Alto CA. Nonlinear estimation of resonant modes in large space structures.

Summer 1980: Computer Audio Research Lab, Center for Music Experiment, University of California, San Diego. Implementation of programs for digital filter design, sampling rate conversion, linear prediction, spectrum analysis, and an on-line documentation facility.

Summer 1979: Acoustics Research Dept., Bell Laboratories, Murray Hill NJ. Application of adaptive filters and variable sampling-rate to the Adaptive Delta Modulation speech coder.

Summer 1978: Total Technology, Inc. Identification of the optical transfer function of phosphor screens in image intensifier systems.

Summer 1978: ESL, Inc. Implementation of software for discriminating among several spoken languages on the basis of phoneme statistics.

Summer 1975 to Fall 1977: ESL, Inc. Digital filter design, AM/FM demodulator design and simulation, research on automatic signal detection and classification, theoretical analysis of system performance, determination of the sensitivity of signal measurements to various types of noise and interference.

Summer 1974: IBM Houston Scientific Center. Software for managing power systems analysis tools.

Work Experience

``Curriculum Vitae for Julius O. Smith III'', by Julius O. Smith III, January, 2005 Copyright © 2023-06-21 by Julius O. Smith III Center for Computer Research in Music and Acoustics (CCRMA), Stanford University

``Curriculum Vitae for Julius O. Smith III'', by Julius O. Smith III, January, 2005
Copyright © 2023-06-21 by Julius O. Smith III
Center for Computer Research in Music and Acoustics (CCRMA), Stanford University