### Audio Segmentation

## Context

One of the most difficult tasks in speech processing is to define limits of the phonetic units present in the signal. Phones are strongly co-articulated and there are no clear borders among them, so the link between the linguistic and the acoustic segmentation is not simple to define. It does not matter which code level is chosen (word, syllable, phon): the acoustic variability of speech signal makes difficult all alignment efforts and its ambiguity challenges all proposed definitions.

## Overview

We have developed an audio segmentation algorithm based on the acoustic signal level. If you are interested by python or C code, please contact us.

## Forward backward segmentation

This segmentation is provided by the Forward-Backward Divergence algorithm, which is based on a statistical study of the acoustic signal.

Assuming that speech signal is described by a string of quasi-stationary units, each one is characterized by an auto regressive (AR) Gaussian model. The method consists in performing a detection of changes in AR models. Two AR models M0 and M1 are identified at every instant, and the mutual entropy between these conditionnal laws allows to quantify the distance between them. M0 represents the segment from last break in the signal while M1 is a short sliding window starting also after last break. When the distance between models change more than a certain limit, a new break is declared in the signal. The algorithm detects three sorts of segments: shorts or impulsive, transitory and quasi-stationary. We show here under an example of the segmentation where infra-phonetic units are determined.

Results of speech segmenting algorithm

The use of an a priori segmentation partially removes redundancy for long sounds, and a segment analysis is relevant to locate coarse features. This approach have given interesting results in automatic speech recognition: experiments have shown that segmental duration carry pertinent information.

## Contributors

- Régine André-Obrecht (contact)
- Patrice Pillot
- Julien Pinquier
- Maxime Le Coz
- Jérôme Farinas

### 7 Multilingual Phonetic Decoders Package

### Package description

This package contains a set of 6 multilingual phonetic decoders: English, German, Hindi, Japanese, Mandarin and Spanish. Each decoder was trained on the Oregon Graduate Institute-Multi Language Telephone Speech Corpus.

The models are based on Hidden Model Markov. 10 Gaussians were used for each state. 12 PLP, the energy and their derivative were used for parametrerization. The frequency bank is in the range of the telephone speech: 300-3400 Hz. The overall topology of the models consists in 3 states HMM, with some adjustments considering the average acoustical duration of the phonetic class considered.

The labeling of each phoneme is based on the OGI labeling guide.

A script is provided in the package in order to facilitate the decoders handling. You will need the HTK toolkit installed in order to use it.

### License

This package is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 3 of the License, or (at your option) any later version.

### Download (FTP)

- 6 multilingual decoders, version 1.2 (2008/10/06)
- 7 multilingual decoders, version 1.3 (2013/01/15) : adding of a French large band decoder

If you want to be involved with future development of this package, ask to by add to the user list: just send an email.

Please send any remark, enhancement to Jérôme Farinas

### SAMOPLAY Demo

## Context

Some of the most used SAMOVA algorithms could be tested live in the SAMOPLAY demonstration platform:

- Audio segmentation
- Speech identification
- Music identification
- Tempogram representation

## Contributors

- Julien Pinquier
- Maxime Le Coz
- Jérôme Farinas

### AESEG – Post-processing toolbox for the segmentation of times series

## Context

During a sound event detection task, a speaker separation, or any time series segmentation task, the predictions at the output of a classification system can be unstable, and the segmentation is not necessarily obvious. There are then several ways to split these prediction curves into multiple segments.

The development of the AESEG library came spontaneously when we were confronted with the problem of segmentation of audio events in the context of my thesis, “Deep, semi-semi supervised learning for the classification and localization of sound events.” Which methods should we use? How to find the best segmentation parameters?

## Overview

**Audio Event SEGmentation (AESG)** is a toolkit compiling a set of segmentation methods and optimization algorithms to delineate time series into discrete events. Written in python and accessible under the form of a package, and source code and documentation can be found here: https://github.com/aeseg/aeseg

The output of prediction systems on temporal data is often used as-is, yet the data is often noisy, with a resolution too small to be used with confidence. A simple smoothing step can already improve the results of these systems. In the case of a classification system, the use of a fixed threshold is often insufficient. AESEG thus proposes a set of smoothing and thresholding methods combined with an optimization algorithm to select the best smoothing and thresholding parameters to maximize time prediction systems’ performance.

### Segmentation algorithms

The seven available segmentation algorithms can be divided into two groups, the data-driven methods, and the parametric methods.

#### Data-driven methods

The statistics-based methods are directly based on the statistics extracted from the temporal predictions of each sample. The main advantage of these methods is that they are fast and often efficient.

By directly using the system temporal predictions, the mean is computed and used as a segmentation threshold. Depending on the methods used, the calculation of the average can be done either at the file or dataset level. Moreover, it can also be independent or dependent on the class (the median can also be used).

**Class-independent data-wise average:**The average is computed using the temporal prediction from the whole dataset and the classes.

$$ \forall x \in S, Th=\frac{1}{N}\sum p(x) $$**Class-independent file-wise average:***n*is the size of the prediction from one file. It depends on the precision of the system.

$$\forall x \in S, Th_x= \frac{1}{n} \sum p(x)$$**Class-dependent data-wise average:***c*represent the class and*C*the ensemble of class the system can target.

$$\forall x \in S, \forall c \in C, Th_c = \frac{1}{N} \sum p_c(x)$$**Class-dependent file-wise average:***c*represent the class and*C*the ensemble of class the system can target.*n*is the size of the prediction from one file. It depends on the precision of the system.

$$\forall x \in S, \forall c \in C, Th_{xc} = \frac{1}{n} \sum p_c(x)$$

#### Parametric methods

These methods optimize the segmentation parameters using the dichotomous search or genetic algorithms. The three images below describe the different algorithms currently available.

**Absolute thresholding**

Absolute thresholding refers to applying a unique and arbitrary threshold to the temporal predictions without using their statistics. This naive approach still yields exploitable results that can get close to the best ones in some cases. It is also the approach with the shortest optimization time due to the unique parameter to optimize. Every probability above this threshold will count as a valid part of the segment.

**Hysteresis thresholding**

The hysteresis threshold is used in many fields, such as finance, imaging, electronics, etc. It is a simple method to implement and has proven itself many times over. This method is based on two thresholds. One is used to determine the beginning of a segment and the other (smaller) its end. This algorithm is used when probabilities are** unstable and changing at a high pace**. It should, therefore, decrease the number of events detected by the algorithm and reduce the insertion and deletion rates, giving a better error rate than the Absolute threshold approach.

**Edge thresholding**

Called slope in the toolkit, this method determines the start and end of a segment by detecting a fast change in the probabilities over time. Fist-rising values imply the beginning of a segment while fast decreasing values followed by a plateau imply the end of the segment. This method makes it possible to obtain a single segment from an event repeated several times over a short period, diminishing the final number of small segments.

### Optimization algorithm

The parametric methods regroup together the different algorithms that exploit arbitrary parameters to perform the segmentation. Depending on the number of parameters to tune, the search space growth is exponential and execution time will often exceed reasonable times. The dichotomous search algorithm implemented in AESEG greatly reduce the number of combination to be tested. The user provides the initial search interval for each parameter that needs to be optimized, the optimization system will then start every combination using a coarse resolution. The best value for each parameter is selected and a new – shorter – interval computed around these new values. The process is repeated several times, each time increasing the precision of the final result.

## Application on Sound Event Detection system

This work was initially developed for the DCASE 2018 – task 4 competition which provides 10 seconds audio files. Each of them could include one or several audio events from a set of 10 sound classes. The task was to provide a list of segments correctly delimiting each event contained in these files. AESEG was applied to the output of a temporal classifier to extract sound events under segments. Using AESEG allows us to gain up to 28 points, confirming the importance of good post-processing.

## Contributor

Léo Cances, Thomas Pellegrini, Patrice Guyot

## Projects

LUDAU – **Lightly-supervised and Unsupervised Discovery of Audio Units using Deep Learning**

## Main publication

- L. Cances, P. Guyot and T. Pellegrini, “Evaluation of Post-Processing Algorithms for Polyphonic Sound Event Detection,”
*2019 IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA)*, New Paltz, NY, USA, pp. 318-322, doi: 10.1109/WASPAA.2019.8937143.

- L. Cances, T. Pellegrini, P. Guyot, “Mutli Task Learning and Post Processing Optimization for Sound Event Detection,” 2019
*Detection and Classification of Acoustic Scenes and Events**(DCASE) Challenge*, New York, NY, USA.

## Reference

- X. Xia, R. Togneri, F. Sohel, and D. Huang, “Frame-wise dynamic threshold based polyphonic acoustic event detection,” inProc. Interspeech, Stockholm, 2017, pp.474–478. [Online]. Available:http://dx.doi.org/10.21437/Interspeech.2017-746

- Q. Kong, I. Sobieraj, W. Wang, and M. Plumbley, “Deep neu-ral network baseline for DCASE challenge 2016,” Budapest,2016.

- J. Salamon, B. McFee, P. Li, and J. P. Bello, “Multiple in-stance learning for sound event detection,” DCASE Chal-lenge, Munich, Tech. Rep., 2017.

- A. Mesaros, T. Heittola, T. Virtanen, A. Mesaros, T. Heittola,and T. Virtanen, “Metrics for Polyphonic Sound EventDetection,”Applied Sciences, vol. 6, no. 6, p. 162, May2016. [Online]. Available: http://www.mdpi.com/2076-3417/6/6/162