{"id":7066,"date":"2018-02-01T14:58:20","date_gmt":"2018-02-01T09:28:20","guid":{"rendered":"https:\/\/data-flair.training\/blogs\/?p=7066"},"modified":"2025-07-28T15:20:25","modified_gmt":"2025-07-28T09:50:25","slug":"deep-learning-audio-analysis","status":"publish","type":"post","link":"https:\/\/data-flair.training\/blogs\/deep-learning-audio-analysis\/","title":{"rendered":"Audio Analysis Using Deep Learning &#8211; Application &amp; Data Handling"},"content":{"rendered":"<div class='__iawmlf-post-loop-links' style='display:none;' data-iawmlf-post-links='[{&quot;id&quot;:2043,&quot;href&quot;:&quot;https:\\\/\\\/en.wikipedia.org\\\/wiki\\\/Deep_learning&quot;,&quot;archived_href&quot;:&quot;http:\\\/\\\/web-wp.archive.org\\\/web\\\/20251012001035\\\/https:\\\/\\\/en.wikipedia.org\\\/wiki\\\/Deep_learning&quot;,&quot;redirect_href&quot;:&quot;&quot;,&quot;checks&quot;:[{&quot;date&quot;:&quot;2025-12-10 23:40:34&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2025-12-14 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07:22:11&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-05-06 23:32:29&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-05-10 05:33:41&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-05-13 07:57:37&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-05-16 16:21:43&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-05-19 20:45:17&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-05-23 09:12:31&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-05-28 03:54:18&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-05-31 05:44:34&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-06-04 08:01:00&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-06-07 11:01:35&quot;,&quot;http_code&quot;:404},{&quot;date&quot;:&quot;2026-06-10 13:12:50&quot;,&quot;http_code&quot;:404},{&quot;date&quot;:&quot;2026-06-13 15:05:51&quot;,&quot;http_code&quot;:200}],&quot;broken&quot;:false,&quot;last_checked&quot;:{&quot;date&quot;:&quot;2026-06-13 15:05:51&quot;,&quot;http_code&quot;:200},&quot;process&quot;:&quot;done&quot;}]'><\/div>\n<div>\n<div class=\"\">\n<p>In this Deep Learning Tutorial, we will study Audio Analysis using <a href=\"https:\/\/data-flair.training\/blogs\/deep-learning\/\"><strong>Deep Learning<\/strong><\/a>. Also, will learn data handling in the audio domain with applications of audio processing. As we will use graphs for a better understanding of audio data Analysis.<\/p>\n<h3>Introduction to Audio Analysis<\/h3>\n<\/div>\n<div class=\"\">\n<div class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\">As we are always in contact with audio. Sometimes <span class=\"adverb\">directly<\/span> or <span class=\"qualifier\">maybe<\/span> <span class=\"adverb\">indirectly<\/span>. As our brain works <span class=\"adverb\">continuously<\/span>. Thus, brain process and understands the information. And at last, it provides us information about the environment.<\/div>\n<\/div>\n<div class=\"\">\n<div class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\">Sometimes we catch this audio floating around us and feel something constructive. As there are some devices which help to catch these sounds. Also represents in computer readable format.<\/div>\n<\/div>\n<div class=\"\">\n<div class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\"><em>Examples of these formats are:<\/em><\/div>\n<\/div>\n<div class=\"\">\n<ul>\n<li class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\"><strong>wav<\/strong> (Waveform Audio File) format<\/li>\n<\/ul>\n<\/div>\n<div class=\"\">\n<ul>\n<li class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\"><strong>mp3<\/strong> (MPEG-1 Audio Layer 3) format<\/li>\n<\/ul>\n<\/div>\n<div class=\"\">\n<ul>\n<li class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\"><strong>WMA<\/strong> (Windows Media Audio) format<\/li>\n<\/ul>\n<\/div>\n<\/div>\n<div class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\"><\/div>\n<div class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\">\n<div id=\"attachment_7069\" style=\"width: 310px\" class=\"wp-caption aligncenter\"><a href=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/02\/sound.png\"><img loading=\"lazy\" decoding=\"async\" aria-describedby=\"caption-attachment-7069\" class=\"wp-image-7069 size-full\" src=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/02\/sound.png\" alt=\"Audio Analysis - Audio Format\" width=\"300\" height=\"225\" srcset=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/02\/sound.png 300w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/02\/sound-150x113.png 150w\" sizes=\"auto, (max-width: 300px) 100vw, 300px\" \/><\/a><p id=\"caption-attachment-7069\" class=\"wp-caption-text\">Audio Analysis &#8211; Audio Format<\/p><\/div>\n<\/div>\n<div>\n<div class=\"\"><\/div>\n<div class=\"\">\n<div class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\">If <span class=\"qualifier\">we think<\/span> more and more about audio, at last, there is one conclusion that it is a wave-like format of data. This can be <span class=\"adverb\">pictorially<\/span> represented as follows.<\/div>\n<div class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\"><\/div>\n<div class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\">Let&#8217;s revise <a href=\"https:\/\/data-flair.training\/blogs\/transfer-learning\/\"><strong>Transfer Learning for Deep Learning with CNN<\/strong><\/a><\/div>\n<\/div>\n<div class=\"\">\n<h3 class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\">Data Handling in Audio Domain<\/h3>\n<\/div>\n<div class=\"\">\n<div class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\">For unstructured data formats,\u00a0 there are a couple of preprocessing steps. We need to follow before we present it\u00a0for audio analysis.<\/div>\n<\/div>\n<div class=\"\"><\/div>\n<div class=\"\">\n<div class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\"><span class=\"adverb\">Firstly<\/span> we have to load data into a machine-understandable format. For this, we <span class=\"adverb\">simply<\/span> take values after every specific time steps.<\/div>\n<\/div>\n<div class=\"\"><\/div>\n<div class=\"\">\n<div class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\"><strong>For example\u00a0<\/strong>&#8211; In a 2-second audio file, we extract values at half a second. This <span class=\"passivevoice\">is called<\/span> a sampling of audio data, and the rate at which it <span class=\"passivevoice\">is sampled<\/span> <span class=\"passivevoice\">is called<\/span> the sampling rate.<\/div>\n<\/div>\n<\/div>\n<div class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\"><\/div>\n<div class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\">\n<div id=\"attachment_7070\" style=\"width: 310px\" class=\"wp-caption aligncenter\"><a href=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/02\/sound-1.png\"><img loading=\"lazy\" decoding=\"async\" aria-describedby=\"caption-attachment-7070\" class=\"wp-image-7070 size-full\" src=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/02\/sound-1.png\" alt=\"Audio Analysis - Example\" width=\"300\" height=\"225\" srcset=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/02\/sound-1.png 300w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/02\/sound-1-150x113.png 150w\" sizes=\"auto, (max-width: 300px) 100vw, 300px\" \/><\/a><p id=\"caption-attachment-7070\" class=\"wp-caption-text\">Audio Analysis &#8211; Example<\/p><\/div>\n<\/div>\n<div>\n<div class=\"\"><\/div>\n<div class=\"\">\n<div class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\">We can represent it in another way. As we can convert data into a different domain, <span class=\"adverb\">namely<\/span> frequency domain. When we sample an audio data, we <span class=\"complexword\">require<\/span> much more data points to represent the whole data. Also, the sampling rate should be as high as possible.<\/div>\n<\/div>\n<div class=\"\"><\/div>\n<div class=\"\">\n<div class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\">So, if we represent audio data in frequency domain. Then much less computational space <span class=\"passivevoice\">is required<\/span>. To get an intuition, take a look at the image below<\/div>\n<\/div>\n<\/div>\n<div class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\"><\/div>\n<div class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\">\n<div id=\"attachment_7071\" style=\"width: 1210px\" class=\"wp-caption aligncenter\"><a href=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/02\/Audio-Features-01.jpg\"><img loading=\"lazy\" decoding=\"async\" aria-describedby=\"caption-attachment-7071\" class=\"wp-image-7071 size-full\" src=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/02\/Audio-Features-01.jpg\" alt=\"Audio Analysis - Audio Features\" width=\"1200\" height=\"628\" srcset=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/02\/Audio-Features-01.jpg 1200w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/02\/Audio-Features-01-150x79.jpg 150w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/02\/Audio-Features-01-300x157.jpg 300w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/02\/Audio-Features-01-768x402.jpg 768w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/02\/Audio-Features-01-1024x536.jpg 1024w\" sizes=\"auto, (max-width: 1200px) 100vw, 1200px\" \/><\/a><p id=\"caption-attachment-7071\" class=\"wp-caption-text\">Audio Analysis &#8211; Audio Features<\/p><\/div>\n<\/div>\n<div>\n<div class=\"\"><\/div>\n<div class=\"\">\n<div class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\">Here, we have to separate one audio signal into 3 different pure signals, that can <span class=\"adverb\">easily<\/span> represent as three unique values in a frequency domain.<\/div>\n<\/div>\n<div class=\"\"><\/div>\n<div class=\"\">\n<div class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\">Also, there are present few more ways in which we can represent audio data and its audio analysis.<\/div>\n<\/div>\n<div class=\"\"><\/div>\n<div class=\"\">\n<div class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\">For example. using MFCs. These are nothing but different ways to represent the data.<\/div>\n<\/div>\n<div class=\"\"><\/div>\n<div class=\"\">\n<div class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\">Further, we have to extract features from this audio representations. This algorithm works on these features and performs the task. Here\u2019s a visual representation of the categories of audio features that can <span class=\"passivevoice\">be extracted<\/span>.<\/div>\n<\/div>\n<\/div>\n<div class=\"\"><\/div>\n<div>Another aspect of audio data shoulder is normalization where the possibility of the variety of audio samples volume is equalized. This is important for preserving the features of a given audio since amplitude variations can distort the features extracted from the audio. On the other hand, normalization helps enhance the optimum performance of machine learning models as it provides equal training and testing datasets.<\/div>\n<div><\/div>\n<div>One of the other key actions here is noise reduction. In real-life conditions, there is always extraneous noise thatspurs from the audio recording and affects the analysis. They include methods like spectral gating and band-pass filtering to help reduce this kind of noise. These preprocessing steps filter out unnecessary information from the audio and enable more accurate detection of the required features and characteristics, thus improving the quality of audio processing applications.<\/div>\n<div><\/div>\n<div>\n<div class=\"\">\n<div class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\">After extracting, we have to send this to the <a href=\"https:\/\/data-flair.training\/blogs\/machine-learning-tutorial\/\"><strong>machine learning<\/strong><\/a> model for further analysis.<\/div>\n<\/div>\n<\/div>\n<div>\n<div class=\"\">\n<h3 class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\">Applications of Audio Processing<\/h3>\n<\/div>\n<div class=\"\">\n<ul>\n<li class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\">Indexing music collections according to their audio features.<\/li>\n<\/ul>\n<\/div>\n<div class=\"\">\n<ul>\n<li class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\">Recommending music for radio channels<\/li>\n<\/ul>\n<\/div>\n<div class=\"\">\n<ul>\n<li class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\">Similarity search for audio files (aka Shazam)<\/li>\n<\/ul>\n<\/div>\n<div class=\"\">\n<ul>\n<li class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\">Speech processing and synthesis \u2013 generating artificial voice for conversational agents<\/li>\n<\/ul>\n<\/div>\n<div class=\"\">\n<p class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\">Let&#8217;s discuss <a href=\"https:\/\/data-flair.training\/blogs\/deep-learning-vs-machine-learning\/\"><strong>Machine Learning Vs Deep Learning<\/strong><\/a><\/p>\n<h3 class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\">Conclusion<\/h3>\n<\/div>\n<div class=\"\">\n<div class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\">Audio analysis is the process of teaching machines to understand sounds. With deep learning, this becomes possible by converting sound into numbers and feeding them into neural networks. Audio is first recorded in the form of waveforms, which are signals that show how sound changes over time. These waveforms are usually converted into spectrograms\u2014visual pictures of sound that show frequency and time.<\/div>\n<div><\/div>\n<div class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\">Deep learning models, especially Convolutional Neural Networks (CNNs), can read these spectrograms like images and learn to recognize different sounds such as speech, music, noise, or even animal voices.<\/div>\n<\/div>\n<\/div>\n<div><\/div>\n<div>Hope you liked the tutorial.<\/div>\n<div>\n<div class=\"\">\n<div class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\"><\/div>\n<div class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\"><a href=\"https:\/\/en.wikipedia.org\/wiki\/Deep_learning\"><strong>For reference<\/strong><\/a><\/div>\n<\/div>\n<\/div>\n","protected":false},"excerpt":{"rendered":"<p>In this Deep Learning Tutorial, we will study Audio Analysis using Deep Learning. Also, will learn data handling in the audio domain with applications of audio processing. As we will use graphs for a&#46;&#46;&#46;<\/p>\n","protected":false},"author":5,"featured_media":8694,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[36],"tags":[103,1026,1228,1229,1230,1231,1233,3658,3661,5095,7006,12898],"class_list":["post-7066","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-machine-learning","tag-data-handling-in-audio-domain","tag-applications-of-audio-processing","tag-audio-analysis","tag-audio-analysis-using-deep-learning","tag-audio-data-analysis","tag-audio-features","tag-audio-visual-sppech-recognistion-with-deep-learning","tag-deep-learning-audio-analysis","tag-deep-learning-for-audio","tag-getting-started-with-audio-data-analysis-using-deep-learning","tag-introduction-to-audio-analysis","tag-simply-audio-recognisition"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.4 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>Audio Analysis Using Deep Learning - Application &amp; Data Handling - DataFlair<\/title>\n<meta name=\"description\" content=\"Learn about Audio Analysis Using Deep Learning, Data Handling in Audio Domain, Applications of Audio Processing and audio data analysis\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/data-flair.training\/blogs\/deep-learning-audio-analysis\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Audio Analysis Using Deep Learning - Application &amp; Data Handling - DataFlair\" \/>\n<meta property=\"og:description\" content=\"Learn about Audio Analysis Using Deep Learning, Data Handling in Audio Domain, Applications of Audio Processing and audio data analysis\" \/>\n<meta property=\"og:url\" content=\"https:\/\/data-flair.training\/blogs\/deep-learning-audio-analysis\/\" \/>\n<meta property=\"og:site_name\" content=\"DataFlair\" \/>\n<meta property=\"article:publisher\" content=\"https:\/\/www.facebook.com\/DataFlairWS\/\" \/>\n<meta property=\"article:published_time\" content=\"2018-02-01T09:28:20+00:00\" \/>\n<meta property=\"article:modified_time\" content=\"2025-07-28T09:50:25+00:00\" \/>\n<meta property=\"og:image\" content=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/02\/Introduction-to-Audio-Data-Analysis-using-Deep-learning-01-1.jpg\" \/>\n\t<meta property=\"og:image:width\" content=\"1200\" \/>\n\t<meta property=\"og:image:height\" content=\"628\" \/>\n\t<meta property=\"og:image:type\" content=\"image\/jpeg\" \/>\n<meta name=\"author\" content=\"DataFlair Team\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:creator\" content=\"@DataFlairWS\" \/>\n<meta name=\"twitter:site\" content=\"@DataFlairWS\" \/>\n<meta name=\"twitter:label1\" content=\"Written by\" \/>\n\t<meta name=\"twitter:data1\" content=\"DataFlair Team\" \/>\n\t<meta name=\"twitter:label2\" content=\"Est. reading time\" \/>\n\t<meta name=\"twitter:data2\" content=\"4 minutes\" \/>\n<!-- \/ Yoast SEO plugin. -->","yoast_head_json":{"title":"Audio Analysis Using Deep Learning - Application &amp; 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