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Module_SignalProcessing
Location: Home / Signal processing module
This module provides rich signal processing across audio effects, feature extraction, filtering, Fourier transforms, common operations, signal definition and manipulation, transforms, and window functions for a wide range of signal processing tasks.
0.9 migration note: Prefer
Signal(Vorcyc.Mathematics.SignalProcessing.Signals) for time-domain real signals. Filtering, transforms, modulation, feature extraction, effects, and other submodules provideSignaloverloads; legacyDiscreteSignalAPIs remain but are[Obsolete]. See Signals and Operations for operations and extension methods.
0.10.1 reorganization: The Fourier family is consolidated under
Vorcyc.Mathematics.SignalProcessing.Fourier—Fft,Fft64,RealFft,RealFft64,Stft, and the sharedFftButterflyFp32/FftButterflyFp64SIMD/parallel kernels all live there now.RealOnlyFFT_Fp32has been removed; useRealFft(orFftfor full complex I/O) instead. See Fourier transforms.
0.10.7: Mode decomposition (EMD / EEMD / CEEMDAN / VMD / … / HHT) is under
Transforms.ModeDecomposition; see Transform operations.
Root namespace for all types in this module:
Vorcyc.Mathematics.SignalProcessing
📒 Organized into the following categories:
- 🔖 Audio effects
- 🔖 Feature extractors
- 🔖 Other audio feature computation
- 🔖 Filters
- 🔖 Fourier transforms
- 🔖 Common operations
- 🔖 Signal definition and related operations
- 🔖 Transform operations
- 🔖 Window functions
- 🔖 Other
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- 🔖 ArrayExtension class
- 🔖 BaseConverter class
- 🔖 BitMathExtension class
- 🔖 Combinatorics class
- 🔖 ConstantsFp32 class
- 🔖 NumberMapper class
- 🔖 PinnableArray<T> class
- 🔖 SimpleRNG_Fp32 and SimpleRNG_Fp64 classes
- 🔖 TrigonometryHelper class
- 🔖 VMath class
- 🔖 Pinned buffers (Buffers)
- 🔖 IPinnedBuffer<T> interface
- 🔖 POHBuffer<T> class
- 🔖 NativeBuffer<T> class
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- 🔖 0.9 classifier walkthrough
- 🔖 KNN<T> class
- 🔖 MultipleLinearRegression<T> class
- 🔖 SimpleLinearRegression<T> class
- 🔖 SupportVectorMachine<TSelf> class
- 🔖 DBSCAN<T> class
- 🔖 ExpectationMaximization<T> class
- 🔖 GMM<T> class
- 🔖 HierarchicalClustering<T> class
- 🔖 KMeansClusterer<T> class
- 🔖 VectorQuantization<T> class
- 🔖 FactorAnalysis<T> class
- 🔖 PCA<TSelf> class
- 🔖 TSNE<T> class
- 🔖 Distance metric classes
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📒 Examples
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📒 GPU 策略说明
- 🔖 为何放弃主包 GPU
- 🔖 当前性能路径
- 🔖 若仍需要更强算力
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📒 计算上下文模块
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📒 核心模块
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📒 深度学习模块
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📒 线性代数模块
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📒 机器学习模块
- 🔖 0.9 分类器完整示例
- 🔖 KNN<T> 类
- 🔖 MultipleLinearRegression<T> 类
- 🔖 SimpleLinearRegression<T> 类
- 🔖 SupportVectorMachine<TSelf> 类
- 🔖 DBSCAN<T> 类
- 🔖 ExpectationMaximization<T> 类
- 🔖 GMM<T> 类
- 🔖 HierarchicalClustering<T> 类
- 🔖 KMeansClusterer<T> 类
- 🔖 VectorQuantization<T> 类
- 🔖 FactorAnalysis<T> 类
- 🔖 PCA<TSelf> 类
- 🔖 TSNE<T> 类
- 🔖 距离度量类
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📒 信号处理模块
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📒 数值模块
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📒 统计模块
- 🔖 基础统计函数
- 🔖 高级统计函数
- 🔖 稳健统计 / RunningMoments
- 🔖 其它统计函数
- 🔖 分布相关函数
- 🔖 时间序列分析
- 🔖 IComparable 扩展
- 🔖 ExtremeValueFinder
- 🔖 INumber 扩展
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📒 FFTW 扩展
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📒 示例