/**
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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 { Tensor, Tensor1D } from '../tensor';
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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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declare function hannWindow_(windowLength: number): Tensor1D;
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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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declare function hammingWindow_(windowLength: number): Tensor1D;
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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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declare function frame_(signal: Tensor1D, frameLength: number, frameStep: number, padEnd?: boolean, padValue?: number): Tensor;
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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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declare function stft_(signal: Tensor1D, frameLength: number, frameStep: number, fftLength?: number, windowFn?: (length: number) => Tensor1D): Tensor;
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export declare const hannWindow: typeof hannWindow_;
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export declare const hammingWindow: typeof hammingWindow_;
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export declare const frame: typeof frame_;
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export declare const stft: typeof stft_;
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export {};
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