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Amit Yadav
In this guided project, we are going to create a deep learning model and train it to learn to classify audio files. Audio classification usually does not get the same kind of attention as image classification with deep learning - this could be because audio processing that is typically used in such scenarios is not as straight forward as image data. In this project, we will look at one such processing to convert raw audio into spectrograms before using them in a convolutional neural network.
You will need prior programming experience in Python. Some experience with TensorFlow is recommended. This is a practical, hands on guided...
Read more
In this guided project, we are going to create a deep learning model and train it to learn to classify audio files. Audio classification usually does not get the same kind of attention as image classification with deep learning - this could be because audio processing that is typically used in such scenarios is not as straight forward as image data. In this project, we will look at one such processing to convert raw audio into spectrograms before using them in a convolutional neural network.
You will need prior programming experience in Python. Some experience with TensorFlow is recommended. This is a practical, hands on guided...
Read more
In this guided project, we are going to create a deep learning model and train it to learn to classify audio files. Audio classification usually does not get the same kind of attention as image classification with deep learning - this could be because audio processing that is typically used in such scenarios is not as straight forward as image data. In this project, we will look at one such processing to convert raw audio into spectrograms before using them in a convolutional neural network.
You will need prior programming experience in Python. Some experience with TensorFlow is recommended. This is a practical, hands on guided project for learners who already have theoretical understanding of Neural Networks, convolutional neural networks, and optimization algorithms like gradient descent but want to understand how to use TensorFlow to classify audio.
Note: This course works best for learners who are based in the North America region. We’re currently working on providing the same experience in other regions.
Register for this course and see more details by visiting:
OpenCourser.com/course/9wc85b/audio
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Useful for learners who already have a foundational understanding of Neural Networks, convolutional neural networks, and optimization algorithms
Helps learners understand how to use TensorFlow to classify audio
Does not require prior knowledge of audio processing, making it accessible to learners from various backgrounds
Assumes learners have programming experience in Python
Some experience with TensorFlow is recommended
Currently only available for learners based in the North America region
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Reviews summary
Beginner's audio classification with tensorflow
This course is a beginner-friendly introduction to audio classification with TensorFlow. The feedback indicates that learners have found the course to be well-paced and informative. The course may be suitable for learners just starting with audio classification and those with some prior experience.
Hands-on project with practical implementation of code.
"Must try out course, a newer way of understanding and executing code."
Introductory course for beginners in audio classification.
"Very nice project for a beginner like me."
Some learners may prefer less guidance.
"Would have liked ... less(?) guidance."
Activities
Be better prepared
before
your course. Deepen your understanding
during
and
after
it. Supplement your coursework and achieve mastery of the topics covered
in Audio Classification with TensorFlow with these
activities:
Review convolutional neural networks
Show steps
Strengthen your understanding of convolutional neural networks, a key concept used in audio classification, to enhance your comprehension of the course material.
Browse courses on
Convolutional Neural Networks
Show steps
-
Review your previous notes or take a refresher course on CNNs.
-
Explore online resources and tutorials to deepen your understanding.
-
Implement a simple CNN model to solidify your knowledge.
Read 'Deep Learning with Python'
Show steps
Gain a comprehensive understanding of deep learning principles and practical implementation techniques by reading this foundational book.
View
Deep Learning with Python, Second Edition
on Amazon
Show steps
-
Read the first few chapters to establish a solid foundation.
-
Work through the exercises and examples provided in the book.
-
Implement some of the concepts you learn in your own Python projects.
Join a study group
Show steps
Engage with fellow learners to discuss course concepts, share knowledge, and support each other's learning.
Show steps
-
Find or join a study group dedicated to deep learning or TensorFlow.
-
Actively participate in discussions and contribute your understanding.
-
Collaborate on projects or assignments to enhance your learning experience.
One other activity
Expand to see all activities and additional details
Show all four activities
Organize your course notes and materials
Show steps
Stay organized and maximize retention by compiling and reviewing your notes, assignments, and other course materials regularly.
Show steps
-
Create a dedicated folder or notebook for your course materials.
-
Organize your notes, assignments, and other resources into logical sections.
-
Review your materials periodically to reinforce your understanding.
Review convolutional neural networks
Show steps
Strengthen your understanding of convolutional neural networks, a key concept used in audio classification, to enhance your comprehension of the course material.
Browse courses on
Convolutional Neural Networks
Show steps
Show steps
Show steps
- Review your previous notes or take a refresher course on CNNs.
- Explore online resources and tutorials to deepen your understanding.
- Implement a simple CNN model to solidify your knowledge.
Read 'Deep Learning with Python'
Show steps
Gain a comprehensive understanding of deep learning principles and practical implementation techniques by reading this foundational book.
View
Deep Learning with Python, Second Edition
on Amazon
Show steps
Show steps
Show steps
- Read the first few chapters to establish a solid foundation.
- Work through the exercises and examples provided in the book.
- Implement some of the concepts you learn in your own Python projects.
Join a study group
Show steps
Engage with fellow learners to discuss course concepts, share knowledge, and support each other's learning.
Show steps
Show steps
Show steps
- Find or join a study group dedicated to deep learning or TensorFlow.
- Actively participate in discussions and contribute your understanding.
- Collaborate on projects or assignments to enhance your learning experience.
One other activity
Expand to see all activities and additional details
Show all four activities
Organize your course notes and materials
Show steps
Stay organized and maximize retention by compiling and reviewing your notes, assignments, and other course materials regularly.
Show steps
Show steps
Show steps
- Create a dedicated folder or notebook for your course materials.
- Organize your notes, assignments, and other resources into logical sections.
- Review your materials periodically to reinforce your understanding.
Career center
Learners who complete Audio Classification with TensorFlow will develop knowledge and skills
that may be useful to these careers:
Research Scientist
Research Scientist
Research Scientists conduct research to advance knowledge in various scientific fields. This course may be useful for Research Scientists specializing in audio processing, speech recognition, or music information retrieval. The course provides a strong foundation in deep learning techniques for audio classification, which is essential for these research areas.
Artificial Intelligence Engineer
Artificial Intelligence Engineer
Artificial Intelligence Engineers design and develop AI systems. This course may be useful for Artificial Intelligence Engineers specializing in audio processing or developing AI applications that leverage audio data. The course provides hands-on experience in building deep learning models for audio classification, which is becoming increasingly common in various AI applications.
Audio Programmer
Audio Programmer
Audio Programmers develop software for audio processing and synthesis. This course may be useful for Audio Programmers seeking to specialize in audio classification or develop tools for audio analysis. The course provides hands-on experience in building deep learning models for audio classification, which is becoming increasingly common in various audio software applications.
Machine Learning Researcher
Machine Learning Researcher
Machine Learning Researchers develop new machine learning algorithms and techniques. This course may be useful for Machine Learning Researchers specializing in audio processing or developing new approaches for audio classification. The course provides a strong foundation in deep learning techniques for audio classification, which is essential for advancing the state-of-the-art in this field.
Software Engineer
Software Engineer
Software Engineers design, develop, and maintain software systems. This course may be useful for Software Engineers interested in specializing in audio processing or developing applications that leverage audio data. The course provides hands-on experience in building deep learning models for audio classification, which is becoming increasingly common in various industries.
Audio Engineer
Audio Engineer
Audio Engineers design, build, and maintain audio systems. This course may be useful for Audio Engineers seeking to expand their knowledge and skills in audio processing and analysis. The course will help learners understand techniques for converting audio into spectrograms, which is a valuable tool for audio analysis and manipulation.
Sound Designer
Sound Designer
Sound Designers create and manipulate sounds for use in movies, video games, and other media. This course may be useful for Sound Designers seeking to expand their skills in audio processing and analysis. The course provides hands-on experience in working with audio data and building models for classification tasks, which can be valuable for creating realistic and immersive sound effects.
Data Analyst
Data Analyst
Data Analysts collect, analyze, and interpret data to provide insights for decision-making. This course may be useful for Data Analysts seeking to specialize in audio data analysis or develop analytical solutions for audio-related applications. The course provides hands-on experience in working with audio data and building models for classification tasks.
Musician
Musician
Musicians create and perform music. This course may be useful for Musicians interested in exploring new techniques for music production or understanding the technical aspects of audio processing. The course provides hands-on experience in working with audio data and building models for classification tasks, which can be valuable for creating innovative and engaging musical compositions.
Product Manager
Product Manager
Product Managers oversee the development and launch of products. This course may be useful for Product Managers working on products that incorporate audio features or leverage audio data. The course provides insights into the technical aspects of audio processing and classification, which can be valuable for making informed decisions about product design and development.
Data Scientist
Data Scientist
Data Scientists use data to extract insights and solve business problems. This course may be useful for those pursuing a career as a Data Scientist, as it provides experience in data analysis and modeling techniques that are commonly used in this field. Learners will gain knowledge in working with audio data, which is increasingly important in various industries.
User Experience Researcher
User Experience Researcher
User Experience Researchers study how users interact with products and services. This course may be useful for User Experience Researchers specializing in audio-based applications or products. The course provides an understanding of audio classification techniques, which can be valuable for designing and evaluating user interfaces that leverage audio data.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers build models to solve complex problems using data, statistics, and algorithms. This course may be useful for those seeking a career as a Machine Learning Engineer, as it provides hands-on experience with audio classification using deep learning techniques. The course will help learners develop skills in data preprocessing, model building, and evaluation, which are essential for success in this role.
Robotics Engineer
Robotics Engineer
Robotics Engineers design, develop, and maintain robots. This course may be useful for Robotics Engineers seeking to incorporate audio processing capabilities into their robots. The course provides hands-on experience in working with audio data and building deep learning models, which can be valuable for developing robots that can interact with their environment through sound.
Computer Vision Engineer
Computer Vision Engineer
Computer Vision Engineers develop and implement computer vision algorithms. This course may be useful for Computer Vision Engineers interested in exploring audio processing and classification techniques. The course provides hands-on experience in working with audio data and building deep learning models, which can be valuable for developing new computer vision applications that leverage audio data.
For more career information including salaries, visit:
OpenCourser.com/course/9wc85b/audio
Reading list
We've selected six books
that we think will supplement your
learning. Use these to
develop background knowledge, enrich your coursework, and gain a
deeper understanding of the topics covered in
Audio Classification with TensorFlow.
Provides a comprehensive overview of deep learning, including the basics of neural networks, convolutional neural networks, and recurrent neural networks. It also covers advanced topics such as generative adversarial networks and reinforcement learning. This book great resource for anyone who wants to learn more about deep learning.
Provides a comprehensive overview of speech and audio processing, including the basics of speech and audio signals, how to process speech and audio signals, and how to use speech and audio processing for a variety of applications. It also covers advanced topics such as speech recognition and audio synthesis.
Provides a comprehensive overview of digital signal processing, including the basics of digital signal processing, how to process digital signals, and how to use digital signal processing for a variety of applications. It also covers advanced topics such as digital filter design and digital image processing.
Provides a comprehensive overview of the mathematics used in machine learning, including the basics of linear algebra, calculus, and probability theory. It also covers advanced topics such as optimization and Bayesian inference.
Provides a comprehensive overview of Python for data analysis, including the basics of Python, how to use Python for data analysis, and how to use Python for a variety of data analysis applications. It also covers advanced topics such as data visualization and machine learning.
Provides a comprehensive overview of machine learning with R, including the basics of R, how to use R for machine learning, and how to use R for a variety of machine learning applications. It also covers advanced topics such as deep learning and natural language processing.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/9wc85b/audio
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Time
2 hours
Via
Coursera
Institution
Coursera Project Network
Instructor
Amit Yadav
Good to know
Useful for learners who already have a foundational understanding of Neural Networks, convolutional neural networks, and optimization algorithms
Helps learners understand how to use TensorFlow to classify audio
Does not require prior knowledge of audio processing, making it accessible to learners from various backgrounds
Assumes learners have programming experience in Python
Some experience with TensorFlow is recommended
Currently only available for learners based in the North America region
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