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getFloatFrequencyData vs getByteFrequencyData
(version: 0)
Benchmark.js
Comparing performance of:
getFloatFrequencyData vs getByteFrequencyData
Created:
3 years ago
by:
Guest
Go to the latest result
Script Preparation code:
var ctx = new AudioContext(); var gain = ctx.createGain(); var oscillator = ctx.createOscillator(); var analyser = ctx.createAnalyser(); gain.gain.value = 0.01; oscillator.frequency.value = 20; oscillator.type = "square"; analyser.fftSize = 32768; var fbuf = new Float32Array(analyser.frequencyBinCount); var bbuf = new Uint8Array(analyser.frequencyBinCount); oscillator.connect(analyser); analyser.connect(gain); gain.connect(ctx.destination); oscillator.start();
Tests:
getFloatFrequencyData
analyser.getFloatFrequencyData(fbuf);
getByteFrequencyData
analyser.getByteFrequencyData(bbuf);
Rendered benchmark preparation results:
Suite status:
<idle, ready to run>
Run tests (2)
Previous results
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Test case name
Result
getFloatFrequencyData
getByteFrequencyData
Fastest:
N/A
Slowest:
N/A
Latest run results:
Run details:
(Test run date:
5 months ago
)
User agent:
Mozilla/5.0 (Android 16; Mobile; rv:149.0) Gecko/149.0 Firefox/149.0
Browser/OS:
Firefox Mobile 149 on Android
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Embed Benchmark Result
<iframe src="https://measurethat.net/Embed?id=627285" width="100%" height="500px"></iframe>
getFloatFrequencyData
683/s
getByteFrequencyData
641/s
View exact numbers
Test name
Executions per second
✓
getFloatFrequencyData
683 Ops/sec
getByteFrequencyData
641 Ops/sec
Autogenerated LLM Summary
(model
llama3.2:3b
, generated one year ago):
Let's break down the provided benchmark and explain what is being tested. **Benchmark Definition** The provided JSON represents a benchmark test between two JavaScript methods: `analyser.getFloatFrequencyData(fbuf)` and `analyser.getByteFrequencyData(bbuf)`. These methods are part of the Web Audio API, which allows developers to manipulate audio signals in web applications. **Options Compared** Two options are being compared: 1. `getFloatFrequencyData`: This method returns an array of floating-point values representing the frequency spectrum of the input signal. 2. `getByteFrequencyData`: This method returns an array of unsigned 8-bit integer values representing the frequency spectrum of the input signal. **Pros and Cons** The choice between these two methods depends on the specific use case: * `getFloatFrequencyData` is generally preferred when: + Higher accuracy is required due to the nature of floating-point arithmetic. + The data will be used for numerical analysis or scientific applications where precision matters. * `getByteFrequencyData` is often preferred when: + Memory efficiency is crucial, as it requires less storage space compared to `Float32Array`. + The data will be used in a context where the values are not subject to precise numerical calculations (e.g., games with pixel-perfect timing). **Library and Purpose** The Web Audio API is a W3C standard for playing and manipulating audio in web applications. It provides an efficient way to generate, manipulate, and analyze audio signals using JavaScript. In this benchmark, the `Analyser` class is used to compute the frequency spectrum of the input signal. The `getFloatFrequencyData` method returns an array of floating-point values, which can be processed further for numerical analysis or visualization. The `getByteFrequencyData` method returns an array of unsigned 8-bit integer values, which are more memory-efficient but may not offer the same level of precision as floating-point numbers. **Special JS Feature/Syntax** There is no special JavaScript feature or syntax used in this benchmark. It only relies on standard Web Audio API functionality and basic JavaScript syntax for creating and manipulating audio signals. **Other Alternatives** If you need to compare performance between these two methods, you can also consider using other approaches: * Using a different library or framework that provides similar functionality (e.g., `Pulse` or `Aria`). * Implementing the frequency analysis algorithm from scratch using algorithms like FFT (Fast Fourier Transform) or Cooley-Tukey. * Using specialized hardware acceleration for audio processing, such as GPU-based implementations. Keep in mind that these alternatives may introduce additional complexity and dependencies not present in this specific benchmark.
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