/**
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* @license
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* Copyright 2019 Google LLC. All Rights Reserved.
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* Licensed under the Apache License, Version 2.0 (the "License");
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* you may not use this file except in compliance with the License.
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* You may obtain a copy of the License at
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*
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* http://www.apache.org/licenses/LICENSE-2.0
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*
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* Unless required by applicable law or agreed to in writing, software
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* distributed under the License is distributed on an "AS IS" BASIS,
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* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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* See the License for the specific language governing permissions and
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* limitations under the License.
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* =============================================================================
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*/
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import {op} from '../ops/operation';
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import {Tensor, Tensor1D} from '../tensor';
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import {mul} from './binary_ops';
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import {concat} from './concat_split';
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import {slice} from './slice';
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import {rfft} from './spectral_ops';
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import {fill, tensor1d, tensor2d} from './tensor_ops';
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/**
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* Generate a Hann window.
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*
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* See: https://en.wikipedia.org/wiki/Window_function#Hann_and_Hamming_windows
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*
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* ```js
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* tf.signal.hannWindow(10).print();
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* ```
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* @param The length of window
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*/
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/**
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* @doc {heading: 'Operations', subheading: 'Signal', namespace: 'signal'}
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*/
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function hannWindow_(windowLength: number): Tensor1D {
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return cosineWindow(windowLength, 0.5, 0.5);
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}
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/**
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* Generate a hamming window.
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*
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* See: https://en.wikipedia.org/wiki/Window_function#Hann_and_Hamming_windows
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*
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* ```js
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* tf.signal.hammingWindow(10).print();
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* ```
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* @param The length of window
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*/
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/**
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* @doc {heading: 'Operations', subheading: 'Signal', namespace: 'signal'}
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*/
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function hammingWindow_(windowLength: number): Tensor1D {
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return cosineWindow(windowLength, 0.54, 0.46);
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}
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/**
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* Expands input into frames of frameLength.
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* Slides a window size with frameStep.
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*
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* ```js
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* tf.signal.frame([1, 2, 3], 2, 1).print();
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* ```
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* @param signal The input tensor to be expanded
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* @param frameLength Length of each frame
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* @param frameStep The frame hop size in samples.
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* @param padEnd Whether to pad the end of signal with padValue.
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* @param padValue An number to use where the input signal does
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* not exist when padEnd is True.
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*/
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/**
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* @doc {heading: 'Operations', subheading: 'Signal', namespace: 'signal'}
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*/
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function frame_(
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signal: Tensor1D, frameLength: number, frameStep: number, padEnd = false,
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padValue = 0): Tensor {
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let start = 0;
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const output: Tensor[] = [];
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while (start + frameLength <= signal.size) {
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output.push(slice(signal, start, frameLength));
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start += frameStep;
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}
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if (padEnd) {
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while (start < signal.size) {
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const padLen = (start + frameLength) - signal.size;
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const pad = concat(
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[slice(signal, start, frameLength - padLen),
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fill([padLen], padValue)]);
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output.push(pad);
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start += frameStep;
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}
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}
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if (output.length === 0) {
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return tensor2d([], [0, frameLength]);
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}
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return concat(output).as2D(output.length, frameLength);
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}
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/**
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* Computes the Short-time Fourier Transform of signals
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* See: https://en.wikipedia.org/wiki/Short-time_Fourier_transform
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*
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* ```js
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* const input = tf.tensor1d([1, 1, 1, 1, 1])
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* tf.signal.stft(input, 3, 1).print();
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* ```
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* @param signal 1-dimensional real value tensor.
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* @param frameLength The window length of samples.
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* @param frameStep The number of samples to step.
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* @param fftLength The size of the FFT to apply.
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* @param windowFn A callable that takes a window length and returns 1-d tensor.
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*/
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/**
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* @doc {heading: 'Operations', subheading: 'Signal', namespace: 'signal'}
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*/
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function stft_(
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signal: Tensor1D, frameLength: number, frameStep: number,
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fftLength?: number,
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windowFn: (length: number) => Tensor1D = hannWindow): Tensor {
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if (fftLength == null) {
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fftLength = enclosingPowerOfTwo(frameLength);
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}
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const framedSignal = frame(signal, frameLength, frameStep);
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const windowedSignal = mul(framedSignal, windowFn(frameLength));
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const output: Tensor[] = [];
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for (let i = 0; i < framedSignal.shape[0]; i++) {
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output.push(rfft(windowedSignal.slice([i, 0], [1, frameLength]),
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fftLength));
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}
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return concat(output);
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}
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function enclosingPowerOfTwo(value: number) {
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// Return 2**N for integer N such that 2**N >= value.
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return Math.floor(Math.pow(2, Math.ceil(Math.log(value) / Math.log(2.0))));
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}
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function cosineWindow(windowLength: number, a: number, b: number): Tensor1D {
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const even = 1 - windowLength % 2;
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const newValues = new Float32Array(windowLength);
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for (let i = 0; i < windowLength; ++i) {
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const cosArg = (2.0 * Math.PI * i) / (windowLength + even - 1);
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newValues[i] = a - b * Math.cos(cosArg);
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}
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return tensor1d(newValues, 'float32');
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}
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export const hannWindow = op({hannWindow_});
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export const hammingWindow = op({hammingWindow_});
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export const frame = op({frame_});
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export const stft = op({stft_});
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