/**
 * Create Mel filterbank matrix
 * @param {number} sr - Sample rate
 * @param {number} n_fft - FFT size
 * @param {number} n_mels - Number of Mel filters
 * @param {number} fmin - Minimum frequency
 * @param {number|null} fmax - Maximum frequency (sr/2 if null)
 * @param {string|number|null} norm - Normalization type ('slaney', number, or null)
 * @param {boolean} htk - Use HTK formula for mel conversion
 * @returns {Array} Mel filterbank matrix (n_mels x n_freq_bins)
 */
export function mel_filterbank(sr?: number, n_fft?: number, n_mels?: number, fmin?: number, fmax?: number | null, norm?: string | number | null, htk?: boolean): any[];
/**
 * Convert Hz to Mel scale
 * @param {number} hz - Frequency in Hz
 * @param {boolean} htk - Use HTK formula instead of Slaney (default: false)
 * @returns {number} Frequency in Mel scale
 */
export function hz_to_mel(hz: number, htk?: boolean): number;
/**
 * Convert Mel scale to Hz
 * @param {number} mel - Frequency in Mel scale
 * @param {boolean} htk - Use HTK formula instead of Slaney (default: false)
 * @returns {number} Frequency in Hz
 */
export function mel_to_hz(mel: number, htk?: boolean): number;
/**
 * Create linearly spaced array
 * @param {number} start - Start value
 * @param {number} stop - Stop value
 * @param {number} num - Number of samples
 * @returns {Array} Linearly spaced array
 */
export function linspace(start: number, stop: number, num: number): any[];
/**
 * Compute Mel spectrogram
 * @param {Float32Array} y - Audio signal (optional if S provided)
 * @param {number} sr - Sample rate
 * @param {Array} S - Pre-computed power spectrogram [freq][time] (optional)
 * @param {number} n_fft - FFT size
 * @param {number} hop_length - Hop length
 * @param {number} win_length - Window length
 * @param {string} window - Window type
 * @param {boolean} center - Center frames
 * @param {string} pad_mode - Padding mode
 * @param {number} power - Exponent for magnitude to power conversion (2.0 = power, 1.0 = magnitude)
 * @param {number} n_mels - Number of Mel filters
 * @param {number} fmin - Minimum frequency
 * @param {number|null} fmax - Maximum frequency
 * @param {string|number|null} norm - Mel filterbank normalization
 * @param {boolean} htk - Use HTK formula for mel conversion
 * @returns {Array} Mel spectrogram [n_mels][n_frames]
 */
export function melspectrogram(y?: Float32Array, sr?: number, S?: any[], n_fft?: number, hop_length?: number, win_length?: number, window?: string, center?: boolean, pad_mode?: string, power?: number, n_mels?: number, fmin?: number, fmax?: number | null, norm?: string | number | null, htk?: boolean): any[];
/**
 * Compute Mel-Frequency Cepstral Coefficients (MFCCs).
 *
 * Wave-4: delegates to the fixture-verified feature/mfcc.js pipeline
 * (power_to_db instead of the old natural log, cached ortho DCT-II basis
 * instead of per-frame O(N²) recomputation; gated by
 * committed reference fixtures).
 *
 * @param {Float32Array} y - Audio signal (optional if S provided)
 * @param {number} sr - Sample rate
 * @param {Array} S - Pre-computed mel POWER spectrogram [n_mels][n_frames]
 *   (this shim's historical semantics: dB conversion is applied for you;
 *   feature/mfcc.js takes a log-power mel spectrogram)
 * @param {number} n_mfcc - Number of MFCCs to return
 * @param {number} dct_type - DCT type (only 2 is supported)
 * @param {string|null} norm - DCT normalization ('ortho' or null)
 * @param {number} lifter - Liftering coefficient (0 = no liftering)
 * @param {number} n_fft - FFT size
 * @param {number} hop_length - Hop length
 * @param {number} win_length - Window length
 * @param {string} window - Window type
 * @param {boolean} center - Center frames
 * @param {string} pad_mode - Padding mode
 * @param {number} power - Exponent for magnitude to power conversion
 * @param {number} n_mels - Number of Mel filters
 * @param {number} fmin - Minimum frequency
 * @param {number|null} fmax - Maximum frequency
 * @param {string|number|null} mel_norm - Mel filterbank normalization
 * @param {boolean} htk - Use HTK formula for mel conversion
 * @returns {Array} MFCC matrix (n_mfcc x n_frames)
 */
export function mfcc(y?: Float32Array, sr?: number, S?: any[], n_mfcc?: number, dct_type?: number, norm?: string | null, lifter?: number, n_fft?: number, hop_length?: number, win_length?: number, window?: string, center?: boolean, pad_mode?: string, power?: number, n_mels?: number, fmin?: number, fmax?: number | null, mel_norm?: string | number | null, htk?: boolean): any[];
/**
 * Discrete Cosine Transform
 * @param {Array} signal - Input signal
 * @param {number} type - DCT type (1, 2, or 3)
 * @param {string|null} norm - Normalization ('ortho' or null)
 * @returns {Array} DCT coefficients
 */
export function dct(signal: any[], type?: number, norm?: string | null): any[];
/**
 * Inverse Discrete Cosine Transform
 * @param {Array} dct_coeffs - DCT coefficients
 * @param {number} type - DCT type (1, 2, or 3)
 * @param {string|null} norm - Normalization ('ortho' or null)
 * @returns {Array} Reconstructed signal
 */
export function idct(dct_coeffs: any[], type?: number, norm?: string | null): any[];
/**
 * Compute delta (first-order derivative) features
 * @param {Array} features - Feature matrix (n_features x n_frames)
 * @param {number} width - Width of delta calculation window
 * @returns {Array} Delta features
 */
export function delta_features(features: any[], width?: number): any[];
/**
 * Lifter MFCC coefficients (apply liftering window)
 * @param {Array} mfcc_matrix - MFCC matrix
 * @param {number} L - Liftering parameter
 * @returns {Array} Liftered MFCC matrix
 */
export function lifter_mfcc(mfcc_matrix: any[], L?: number): any[];
/**
 * Convert power spectrogram to dB scale
 * @param {Array} power_spec - Power spectrogram
 * @param {number} ref - Reference power level
 * @param {number} amin - Minimum amplitude
 * @param {number} top_db - Maximum dB range
 * @returns {Array} dB spectrogram
 */
export function power_to_db(power_spec: any[], ref?: number, amin?: number, top_db?: number): any[];
/**
 * Get Mel frequencies for given number of filters
 * @param {number} n_mels - Number of Mel filters
 * @param {number} fmin - Minimum frequency
 * @param {number} fmax - Maximum frequency
 * @returns {Array} Mel frequencies
 */
export function mel_frequencies(n_mels: number, fmin?: number, fmax?: number): any[];
/**
 * Simple feature extraction for audio classification
 * @param {Float32Array} y - Audio signal
 * @param {number} sr - Sample rate
 * @returns {Object} Extracted features
 */
export function extract_mel_features(y: Float32Array, sr?: number): any;