Off-DSP memory arrays store processor instructions and data. Other functions such as timers and program boot logic ease DSP system development. Having modeled the filter algorithms and looked at some of the DSP architectural features, one is ready to start looking at how these filters could be coded in DSP assembly language.
Up to this point the discussion and examples have been generic, applying to almost all DSPs. This processor is a fixed-point, bit DSP. The term "fixed-point" means that the "point" separating the mantissa and exponent does not change its bit location during arithmetic operations.
This DSP uses bit data words and bit wide instruction words. DSPs are specified by the size of the data, rather than instruction width because data word size describes the width of data that the DSP can handle most efficiently. The software has two parts. The main routine includes register and buffer initialization along with the interrupt vector table, and the interrupt routine that executes when a data sample is ready.
In this example, the processor idles in a low-power standby mode waiting for an interrupt. The FIR filter interrupt subroutine the last segment of code is the heart of the filter program.
The processor responds to the interrupt, saving the context of the main routine and jumping to the interrupt routine. This interrupt routine processes the filter input sample, reading data and filter coefficients from memory and storing them in data registers of the DSP processor. Figure 6.
Note that this program uses DSP features that perform operations with zero overhead, usually introduced by a conditional. In particular, program loops and data buffers are maintained with zero overhead. The program checks the final result of the filter calculation for any overflow. If the final value has overflowed, the value is saturated to emulate the clipping of an analog signal. Finally, the context of the main routine is restored and the instruction flow is returned to the main routine with a return from interrupt RTI instruction.
The goal of this article has been to provide a link between filter theory and digital filter implementation. On the way, this article covers modeling filters with HLL programs, using DSP architecture, and experimenting with filter software. The issues introduced in this article include:. These texts provides a complete overview of DSP theory, implementation issues, and reduction to practice with devices available at the time of publication , plus exercises and examples.
The Reference section below also contains other sources that further amplify this article's issues. Working through this series, each part adds some feature or information contributing to the series goal of developing a DSP system. Noam Levine joined MathWorks in in technical marketing, focusing on Model-Based Design workflows targeting embedded platforms. APR Information can be used to enhance or improve desired aspects of a signal or even to reduce undesirable aspects.
DSP processes information adaptively. This concept is imperative in a dynamic application such as sound and speech, especially when applied in industrial environments. DSP creates flexibility.
Changes, updates, customizations, and many other features are available with the implementation of DSP systems. DSP allows users to get the job done efficiently, practically, and cost effectively.
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Follow HearSensear. Related posts. Follow Sensear Blog. The Value of Digital Signal Processing. Noise Suppression vs. In the real-world, analog products detect signals such as sound, light, temperature or pressure and manipulate them.
Converters such as an Analog-to-Digital converter then take the real-world signal and turn it into the digital format of 1's and 0's. From here, the DSP takes over by capturing the digitized information and processing it. It then feeds the digitized information back for use in the real world. It does this in one of two ways, either digitally or in an analog format by going through a Digital-to-Analog converter. All of this occurs at very high speeds.
We will come back to this. Next, your signal likely hits some sort of routing matrix. You will want to set up the routing matrix based upon where the signal will go once it leaves the various outputs of the DSP.
Is your system bi-amplified? Will you be running subs? Delay speakers? Finally, your signal will hit a series of processing objects which we will collectively call the output side of the DSP.
This will likely include a crossover consisting of a high pass and low pass filter , EQ, delay, limiter, and polarity switch, usually in that order. It is critical to understand that the parameters on the output side of your DSP are used to deliver the correct frequency content with the correct time offsets to the various components in your loudspeaker system.
These are fixed values that should not change once they are set with the one possible exception being the high pass filter on the sub output , regardless of the environment in which the system is set up.
The reasons for this are numerous and are beyond the scope of this article, but it has to with the phase interaction between the drivers in the crossover range. There are two things you should note about setting up the output side of your DSP. Meyer products provide the same benefit. This is a quick and easy way to get your system up and running.
Though most loudspeaker manufacturers publish the operating parameters for their products, getting the system to operate correctly may not be a simple as plugging the numbers into the processor.
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