How to develop audio recognition software
Introduction
Audio recognition software is also known as Automatic Speech Recognition software and is gaining enormous popularity, thanks to the increase in computing capacity and advancement of Big Data. You will find this technology in smart speakers, medical transcription, and in similar applications. And of course, you might already be familiar with the growth and smartness of Siri and Alexa over the years. The growth in machine learning techniques and artificial intelligence has enabled developers to create software that relates to customer requirements and anticipate and address their needs.
Before developing audio recognition software, it would be a good idea to understand the key components that come into its development. The main purpose of audio recognition software is to identify and interpret sound signals and extract meaningful information from them.
Components of audio recognition software
Audio recognition is a series of steps that makes it possible for the software to function according to expectations. This is how the software will be able to respond to the needs and demands of the customer. Check out the different components inherent to the software:
Signal preprocessing
It is important to enhance the quality of the audio and extract the relevant features from the raw audio signal. This is done through techniques like filtering, noise reduction, normalising, etc.
Feature extraction
This is the next fundamental step in audio software development and it is all about converting raw audio signals into simple, understandable representations for analysis. The software system will learn patterns and make predictions based on the new data.
Pattern recognition
Machine learning algorithms will be trained to extract patterns from the huge influx of data that comes in.
Language model
It is important to distinguish the colloquial expressions and abbreviations of people when they speak and convert them into a standard written form.
There are other components in the software like ‘acoustic model’ which is all about capturing and distinguishing phonetic units from a huge number of datasets based on the speech samples from diverse speakers. Then you have another component, the Lexicon component that helps map the acoustic models to their correct phonetic units.
Process of developing audio recognition software
Some technical aspects dictate the audio properties like automatic speech recognition, natural language processing algorithms, etc. While analysing the duration of the audio clips, it is also important to understand the following in audio properties: the number of channels (stereo or mono), sample rate value (8kHz, 16kHz, etc.), bitrate (e.g., 32 kbit/s, 128 kbit/s), audio file format (e.g., mp3, wav, flac).
Some of the key steps in the process
- Understanding and defining the project goals
Have a clear concept of what kind of audio software you are building! Is your audio software trying to recognise music, natural sounds in the environment, or speech? Or maybe a combination of all those? So defining the project goal is the first step in deciding what kind of audio software is that you are building. This will also dictate the accuracy of the sounds and what critical features the applications must be built on.
- Identifying the target sounds, accuracy and type of processing
Identifying the desired sound from the plethora of sounds can be a very tricky thing, but the technological advancements in detecting only what’s required has made it possible to extract and filter what’s required. The sounds could be sounds that are identified with mechanical failure, traffic sounds, speech commands, musical instruments, and similar ones. Accuracy is also a factor. If you are looking at highly accurate systems you need very large training sets and highly accurate systems. You also need to determine whether the sounds are happening in real time or if they are pre-recorded audio files.
- Data collection and data preprocessing stages are very crucial
The quality and quantity of the data are very important because the software relies on data, and if the data is up to expectations, it will give you a high-performing model. This is where you need to exert importance in data collection, and then labelling them. Once you collect all the important audio samples, you can label them by annotating each audio sample with what category it belongs to. This process promotes supervised learning algorithms, so when the sound is that of a ‘barking dog’, it will be labelled correctly as a ‘dog’.
The next stage is the data preprocessing stage. This is where the audio is polished, cleaned, and prepared. It will make the raw data interpretable by the machine through a process known as ‘feature extraction’. This would clean up the audio and remove any background noises, with correct audio levels.
- Understanding which approach is required for audio recognition
There are a couple of approaches that can be used for audio recognition. If you are looking at simple sounds, then you can go for traditional methods like Support Vector Machines or SVM or Hidden Markov Models (HMMs) or for an approach that is more complex – Deep Learning. If you do not have complex sound recognition tasks and do not have too many sound categories, then the traditional model might be sufficient.
With the Deep Learning approach, you might need to approach artificial neural networks like convolutional neural networks (CNNs) to process the audio. This also requires huge amounts of training data and plenty of computational resources. If the desired accuracy level is extremely high then you might need to go for the Deep Learning method.
- And finally, training the model to different sound categories
So once the data for the audio recognition software is collected, you can train the model so it can distinguish the different sound patterns and categories. The labelled data will be fed into the algorithm so it can learn the patterns. Train, validate, and test the data sets so you can fine-tune them to perfection. It is at this stage that the various hyperparameters within an algorithm will be adjusted and tuned. Post the adjustment and tuning, you can evaluate the metrics so they correctly identify the various sound categories.
And once the tuning and evaluation are done, it is time to release the software into real-world applications. This can be done either in the device or on the cloud.
Conclusion
Developing audio software for a company is a very exciting and rewarding endeavour, but it requires a lot of knowledge, technical skills, and updated knowledge of the latest innovations in the field of machine learning, software engineering, and signal processing expertise. Developers must also be updated on the latest tools and technologies in audio analysis, and with a mastered knowledge of audio recognition, it is possible to open up exciting opportunities that will take artificial intelligence to the next level of success with more innovation and discovery.
You need a team that takes a strategic approach, has a deep understanding of user needs, is aware of the advanced technologies, and has an insightful knowledge of what user needs could be in the future. This would help them create powerful and very effective audio recognition software that will change the concept of using and perceiving technology
Interesting Links:
Building a Voice Recognition Software with Machine Learning
Check out to learn how to get started with Audio Recognition Software Development
Pictures: Canva
The author: Sascha Thattil works at Software-Developer-India.com which is a part of the YUHIRO Group. YUHIRO is a German-Indian enterprise which provides programmers to IT companies, agencies and IT departments.
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