We’ve all heard of text classification, image classification, but have you tried audio classification? Leave classification; there are a ton of other things we can do in audio by using artificial intelligence and deep learning. In this article, we’ll be talking about various speech processing projects.
You can work on these projects to get more familiar with different applications of AI in the audio and sound analysis. From audio classification to recommendation systems for music, there are many project ideas in this list. So, let’s dive in.
Speech Processing Projects & Topics
1. Classify Audio
Audio classification is among the most in-demand speech processing projects. As deep learning focuses on building a network that resembles a human mind, sound recognition is also essential. While image classification has become much advanced and widespread, audio classification is still a relatively new concept.
So, you can work on an audio classification project and get ahead of your peers with ease. You might wonder how you’d start working on an audio classification project, but don’t worry because Google has got your back through AudioSet. AudioSet is a vast collection of labeled audio that they collected from YouTube videos. They all are 10-seconds long and are incredibly varied.
You can use the audio files present in AudioSet to train and test your model. They are correctly labeled, so working with them is relatively more straightforward. There are currently 632 audio event classes and more than two million sound clips present in AudioSet. Check Google AudioSet here.
As a beginner, focus on extracting specific features from an audio file and analyzing it through a neural network. You can use small audio clips to train the neural network.
Use Data Augmentation to avoid overfitting, which would bother you a lot while performing audio classification. Additionally, we recommend using a convolutional neural network, also known as CNN, to perform audio classification. You might also use slowing down or speeding up of sound to suit the needs of your model.
2. Generate Audio Fingerprints
One of the most recent and impressive technologies is audio fingerprinting, that’s why we’ve added it in our list of speech processing projects. When you generate an audio signal by extracting the relevant acoustic features from a piece of audio, then condense the specific audio signal, we call this process audio fingerprinting. You can say that an audio fingerprint is a summary of a particular audio signal. They have the name ‘fingerprint’ in them because every audio fingerprint is unique, just like human fingerprints.
By generating audio fingerprints, you can identify the source of a particular sound at any instance. Shazam is probably the most famous example of an audio fingerprinting application. Shazam is an app that lets people identify songs by listening through a small section of the same.
A common problem in generating audio fingerprints is background noise. While some people use software solutions to eliminate background noise, you can try representing audio in a different format and remove the unnecessary clutter from your file. After that, you can implement the required algorithms to distinguish the fingerprints.
3. Separate Audio Sources
Another prevalent topic among speech processing projects is the separation of audio sources. In simple terms, audio source separation focuses on distinguishing different types of audio source signals present in the midst of signals. You perform audio source separation every day. A rough example of audio source separation in real-life is when you distinguish the lyrics of a song. In that instance, you’re separating the lyrics’ audio signals from the rest of the music. You can use deep learning to perform this as well!
To work on this project, you can use the LibriSpeech and the UrbanNoise8k datasets. The former is a collection of audio clips of people reading books without any background noise, whereas the latter is a collection of background noises. Using both of them, you can easily create a model that can distinguish specific audio signals from one another. You can convert spectrograms to make your job easier.
FYI: Free nlp online course!
Remember to use the loss function as it focuses on what part you have to minimize. Using the loss function, you can teach your model to ignore background noises with much more ease. Here’s an excellent audio source separation app as an example.
4. Segment Audio
Segmenting refers to dividing something into different parts according to their features. So, audio segmentation is when you segment audio signals according to their unique characteristics. It’s a crucial part of speech processing projects, and you’d need to perform audio segmentation on nearly all of the projects we’ve listed here. It’s similar to data cleaning but in the audio format.
An excellent application of audio segmentation is heart monitoring, where you can analyze the sound of heartbeats and separate its two segments for enhanced analysis. Another general application of audio segmentation is in speech recognition, where the system can separate the words from background noise and enhance the performance of the speech recognition software.
Here’s an excellent audio segmentation project published in the MECS press. It discusses the fundamentals of automatic audio segmentation and proposes multiple segmentation architectures for different applications. Going through it would certainly be useful in understanding audio segmentation better.
5. Automated Music Tags
This project is similar to the audio classification project we discussed earlier. However, there’s a slight difference. Music tagging helps in creating metadata for songs so people can find them easily in an extensive database. In music tagging, you have to work with multiple classes. So you have to implement a multi-label classification algorithm. However, as we’ve discussed in previous projects, we start with the basics, aka, the audio features.
Then we’ll use a classifier that separates the audio files according to the similarities in their features. Unlike the audio classification we discussed in the project above, we’ll have to use a multi-label classification algorithm here.
As a form of practice, you should start with the Million Song Dataset, a free collection of popular tracks. The dataset doesn’t have audio, and it only has features, so an extensive section is pre-done. You can train and test your model by using the Million Song dataset easily. Check out the Million Song dataset here.
You can use CNNs to work on this project. Check out this case study, which discusses audio tagging in detail and uses Keras and CNNs for this task.
6. Recommender System for Music
Recommender systems are widely popular these days. From eCommerce to media, nearly every B2C industry is implementing them to reap their benefits. A recommender system suggests products or services to a user according to their past purchases or behavior. Netflix’s recommendation system is probably the most famous among AI professionals and enthusiasts alike. However, unlike Netflix’s recommendation system, your recommendation system would be analyzing audio to predict user behavior. Music streaming platforms such as Spotify are already implementing such recommender systems to enhance user experience.
It’s an advanced-level project which we can divide into the following sections:
- You’ll first have to create an audio classification system that can distinguish a song’s specific features from the other one. This system will analyze the songs our user listens to the most.
- You’ll then have to build a recommendation system that analyzes those features and finds the common attributes among them.
- After that, the audio classification system would find the features present in other songs our user hasn’t listened to yet.
- Once you have those features available, your recommendation system would compare them with its findings and recommend more songs according to them.
While this project may sound a bit complicated, once you’ve built both models, things will get easier.
A recommender system focuses on classification algorithms. If you haven’t created one in the past, you should first practice building one before moving onto this project.
You can also start with a small dataset of songs by classifying them according to the genre or artist. For example, if a user listens to The Weeknd, it’s highly probable they’d listen to other songs present in his genres, such as R&B and Pop. This will help you shorten the database for your recommendation system.
Learn More About Deep Learning
Audio analysis and speech recognition are relatively new technologies than their textual and visual counterparts. However, as you can see in this list, various implementations and possibilities are present in this field. Thanks to artificial intelligence and deep learning, we can expect more advanced audio analysis in the future.
These speech processing projects are just the tip of the iceberg. There are many other applications of data learning available. If you want to explore more deep learning projects, we recommend these resources:
- 13 Neural Network Project ideas
- Top 7 Deep Learning Projects in Github You Should Know
- 16 Exciting Deep Learning Project Ideas
Also, you can take a machine learning and deep learning course to become a proficient expert. The course will provide you with training from industry leaders through projects, videos, and study materials.
What is speech Processing in artificial intelligence?
Speech processing is the computer understanding of the voice. It is the process of turning a speech signal into useful information for users. Speech processing is to turn continuous analog speech signal into discrete digital signal. It is about converting sound waves into information for machine reading. Speech processing is basically a sub-field of computer science that provides methods to convert speech signals into text or other useful data. The most common application of speech processing is to convert speech signals into textual data. In this case, speech processing deals mainly with modeling the speech signal and implementing a suitable speech recognition engine.
Which algorithm is used for speech recognition?
The algorithms for speech recognition are very advanced. These algorithms convert voice signals into text characters. The main speech recognition algorithm is Hidden Markov Model. This algorithm has been implemented in many operating systems like Mac OS, iPhone, Android and others. The speech recognition software works on this particular algorithm by switching between different states. This algorithm will be replaced by the deep learning AI(Artificial Intelligence) in the near future since this algorithm doesn’t require any feature engineering.
What are the applications of speech recognition?
Speech recognition is the process of converting spoken words into text. In areas such as call centers, this can be a very useful technology. A call center professional can deal with multiple calls at once by using speech recognition to dictate the information that goes on the call. Also, in an office setting, speech recognition can be used to type up documents. In addition, this technology can be used in other areas such as gaming. A lot of games now allow users to navigate menus by using their voice.