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Digital Song Analysis Using Frequency Analysis
Digital Song Analysis Using Frequency Analysis Imagine sitting at a café (or "other" public venue) and you hear a song playing on the stereo. You decide that you really like it, but you don't know the name of the song. There's a solution for that. Software song identification has been a topic of interest for years. However, it is computationally difficult to tackle this problem using conventional algorithms. Frequency analysis provides for a fast and accurate solution to this problem, and we decided to use this analysis to come up with a fun project idea. The main purpose of our project was to be able to accurately match a noisy song segment with a song in our song library. The company Shazam was our main inspiration and we started out by studying how Shazam works. Chapter Outline: The Fingerprint of a Song The Fingerprint Finding Algorithm The Resulting Fingerprint Matched Filter for Spectrogram Peaks The Matched Filter Algorithm Results The Open Courses Library introduces you to the best Open Source Courses.
Analysis, Synthesis, and Perception of Musical Sounds
This book contains a complete and accurate mathematical treatment of the sounds of music with an emphasis on musical timbre. The book spans the range from tutorial introduction to advanced research and application to speculative assessment of its various techniques. All the contributors use a generalized additive sine wave model for describing musical timbre which gives a conceptual unity, but is of sufficient utility to be adapted to many different tasks.
Spectral Analysis of Musical Sounds with Emphasis on the Piano
In this book the tools of spectral analysis are applied via graphics to musical sounds, especially those coming from a piano, with emphasis on the visualization of musical sounds rather than the mathematics behind it. The aim is to give a different and insightful view of musical instruments.
Music and Acoustics
How can a piano tuner obtain such high precision with no other measuring device than their own ears? How can a sequence of notes seem to rise continuously despite coming back periodically to the same notes? What are the possibilities and the limits of digital sound? These are a few examples of questions that are discussed in this book, which presents an overview on the nature of musical sounds, from their production by acoustic music instruments to synthesized sounds obtained with computers. The topics that are treated include sound propagation, Fourier and time-frequency analysis, psychoacoustics, analog and digital signal processing theory, computer science and MP3 sound compression, and of course... music!
Signal Analysis of Hindustani Classical Music
This book presents a comprehensive overview of the basics of Hindustani music and the associated signal analysis and technological developments. It begins with an in-depth introduction to musical signal analysis and its current applications, and then moves on to a detailed discussion of the features involved in understanding the musical meaning of the signal in the context of Hindustani music. The components consist of tones, shruti, scales, pitch duration and stability, raga, gharana and musical instruments. The book covers the various technological developments in this field, supplemented with a number of case studies and their analysis. The book offers new music researchers essential insights into the use the automatic concept for finding and testing the musical features for their applications. Intended primarily for postgraduate and PhD students working in the area of scientific research on Hindustani music, as well as other genres where the concepts are applicable, it is also a valuable resource for professionals and researchers in musical signal processing.
Music Data Analysis
This book provides a comprehensive overview of music data analysis, from introductory material to advanced concepts. It covers various applications including transcription and segmentation as well as chord and harmony, instrument and tempo recognition. It also discusses the implementation aspects of music data analysis such as architecture, user interface and hardware. It is ideal for use in university classes with an interest in music data analysis. It also could be used in computer science and statistics as well as musicology.
An Introduction to Audio Content Analysis
An Introduction to Audio Content Analysis Enables readers to understand the algorithmic analysis of musical audio signals with AI-driven approaches An Introduction to Audio Content Analysis serves as a comprehensive guide on audio content analysis explaining how signal processing and machine learning approaches can be utilized for the extraction of musical content from audio. It gives readers the algorithmic understanding to teach a computer to interpret music signals and thus allows for the design of tools for interacting with music. The work ties together topics from audio signal processing and machine learning, showing how to use audio content analysis to pick up musical characteristics a...
