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signal_ops.ts
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/
signal_ops.ts
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/**
* @license
* Copyright 2019 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
import {op} from '../ops/operation';
import {Tensor, Tensor1D} from '../tensor';
import {concat} from './concat_split';
import {slice} from './slice';
import {fill, tensor1d, tensor2d} from './tensor_ops';
/**
* Generate a Hann window.
*
* See: https://en.wikipedia.org/wiki/Window_function#Hann_and_Hamming_windows
*
* ```js
* tf.signal.hannWindow(10).print();
* ```
* @param The length of window
*/
/**
* @doc {heading: 'Operations', subheading: 'Signal', namespace: 'signal'}
*/
function hannWindow_(windowLength: number): Tensor1D {
return cosineWindow(windowLength, 0.5, 0.5);
}
/**
* Generate a hamming window.
*
* See: https://en.wikipedia.org/wiki/Window_function#Hann_and_Hamming_windows
*
* ```js
* tf.signal.hammingWindow(10).print();
* ```
* @param The length of window
*/
/**
* @doc {heading: 'Operations', subheading: 'Signal', namespace: 'signal'}
*/
function hammingWindow_(windowLength: number): Tensor1D {
return cosineWindow(windowLength, 0.54, 0.46);
}
/**
* Expands input into frames of frameLength.
* Slides a window size with frameStep.
*
* ```js
* tf.signal.frame([1, 2, 3], 2, 1).print();
* ```
* @param signal The input tensor to be expanded
* @param frameLength Length of each frame
* @param frameStep The frame hop size in samples.
*/
/**
* @doc {heading: 'Operations', subheading: 'Signal', namespace: 'signal'}
*/
function frame_(
signal: Tensor1D, frameLength: number, frameStep: number, padEnd = false,
padValue = 0): Tensor {
let start = 0;
const output: Tensor[] = [];
while (start + frameLength <= signal.size) {
output.push(slice(signal, start, frameLength));
start += frameStep;
}
if (padEnd) {
const padLen = (start + frameLength) - signal.size;
const pad = concat(
[slice(signal, start, frameLength - padLen), fill([padLen], padValue)]);
output.push(pad);
}
if (output.length === 0) {
return tensor2d([], [0, frameLength]);
}
return concat(output).as2D(output.length, frameLength);
}
function cosineWindow(windowLength: number, a: number, b: number): Tensor1D {
const even = 1 - windowLength % 2;
const newValues = new Float32Array(windowLength);
for (let i = 0; i < windowLength; ++i) {
const cosArg = (2.0 * Math.PI * i) / (windowLength + even - 1);
newValues[i] = a - b * Math.cos(cosArg);
}
return tensor1d(newValues, 'float32');
}
export const hannWindow = op({hannWindow_});
export const hammingWindow = op({hammingWindow_});
export const frame = op({frame_});