/**
 * 2D Convolution for spectrograms
 * @param {Array<Array<number>>} matrix - 2D spectrogram (freq x time)
 * @param {Array<Array<number>>} kernel - 2D convolution kernel
 * @param {string} mode - Padding mode ('constant', 'edge', etc.)
 * @param {number} cval - Constant value for padding
 * @returns {Array<Array<number>>} Convolved matrix
 */
export function convolve2d(matrix: Array<Array<number>>, kernel: Array<Array<number>>, mode?: string, cval?: number): Array<Array<number>>;
/**
 * Downsample spectrum (MaxPool-like)
 * @param {Array<number>} spectrum - Frequency spectrum
 * @param {number} factor - Downsampling factor
 * @returns {Array<number>} Downsampled spectrum
 */
export function downsampleSpectrum(spectrum: Array<number>, factor?: number): Array<number>;
/**
 * Create oriented edge detection filter
 * @param {number} angleDeg - Angle in degrees
 * @param {number} size - Kernel size
 * @returns {Array<Array<number>>} Convolution kernel
 */
export function createOrientedFilter(angleDeg: number, size?: number): Array<Array<number>>;
/**
 * Create 18 different views/slices of the spectrogram
 * @param {Array<Array<number>>} magnitudeSpectrogram - Magnitude spectrogram (freq x time)
 * @returns {Object} Dictionary of slices
 */
export function create18Slices(magnitudeSpectrogram: Array<Array<number>>): any;
/**
 * Extract detailed metrics from a frequency window (425-point fingerprint)
 * @param {Array<number>} window - Frequency spectrum for one time window
 * @param {number} sr - Sample rate
 * @returns {Object} Fingerprint metrics
 */
export function windowToFingerprint(window: Array<number>, sr: number): any;
/**
 * Process audio to create complete fingerprint
 * @param {AudioBuffer} audioBuffer - Audio buffer
 * @param {number} nFft - FFT window size
 * @param {number} hopLength - Hop length
 * @returns {Object} Processing results including fingerprints
 */
export function processAudioToFingerprints(audioBuffer: AudioBuffer, nFft?: number, hopLength?: number): any;
/**
 * Optimize EQ curves to match mixture fingerprints to vocal fingerprints
 * @param {Object} vocalFps - Vocal fingerprints
 * @param {Object} mixtureFps - Mixture fingerprints (used for initialization context)
 * @param {Array<Array<number>>} mixtureMag - Mixture magnitude spectrogram (freq x time)
 * @param {number} numWindows - Number of time windows
 * @param {number} sr - Sample rate
 * @param {number} numIterations - Number of optimization iterations
 * @param {number} learningRate - Learning rate for gradient descent
 * @returns {Array<Array<number>>} Optimized EQ curves
 */
export function optimizeEqCurves(vocalFps: any, mixtureFps: any, mixtureMag: Array<Array<number>>, numWindows: number, sr: number, numIterations?: number, learningRate?: number): Array<Array<number>>;
/**
 * Reconstruct vocal audio using learned EQ curves
 * @param {Array} mixtureStft - Mixture STFT in (freq x time) format
 * @param {Array<Array<number>>} eqCurves - EQ curves for each window
 * @param {number} sr - Sample rate
 * @param {number} nFft - FFT size
 * @param {number} hopLength - Hop length
 * @returns {Float32Array} Reconstructed audio
 */
export function reconstructVocal(mixtureStft: any[], eqCurves: Array<Array<number>>, sr: number, nFft?: number, hopLength?: number): Float32Array;