Audio analysis is the process of breaking down an audio signal into its individual components in order to better understand its characteristics. This can include identifying individual sounds, categorising patterns, and analysing the frequency and amplitude of the signal. Audio analysis is a critical tool in many fields, including environmental monitoring, speech recognition, and noise consultancy. In this guide, we’ll explore the process of audio analysis and how it can help automate a time-consuming task for noise analysts.
The Problem: So many clips, so little time
Despite the remarkable capabilities of standard environmental sensors, advancements in technology and the increasing demand for accuracy and precision highlight a noticeable gap in the capabilities of noise sensors.One of the biggest challenges faced by noise analysts is manually analysing large volumes of data, often several days of continuous audio recording. This process can be time-consuming and tedious, as analysts have to listen to each recording in detail and make notes about any relevant information. This can take hours or even days to complete, depending on the amount of data involved. Manual noise surveying can also mean things get missed, which can be detrimental to monitoring noise pollution. Retrieving and analysing high-resolution data streams often requires a compromise between timeliness and accuracy, which is a pretty big challenge in today’s fast-paced world. On top of that, most alerts from continuous noise identification and measurement come from non-industrial sources, such as bird songs, wind, road traffic, or the operation of domestic equipment. This “alarm flooding” creates significant costs, often resulting in excessive curtailment of activities and diverting attention from genuine noise alerts or production-critical tasks.
The Solution: Automated Audio Analysis
Thankfully, there is a solution to this problem: automated audio analysis. By using software to automatically analyse audio data, noise analysts can save hours of manual annotation work. Think of our Copilot API as an efficient assistant, taking care of the tedious aspects of audio analysis. Once your file is processed by the co-pilot, you get a report that can inform your work. Let’s go through the steps the API takes to deliver an effective audio analysis.
Environmental sounds, such as traffic, wind, and rain, can be major obstacles when it comes to recording high-quality audio. These sounds can often drown out important sounds, making it difficult to capture clear recordings. Fortunately, with the use of AI-based audio decontamination technology, these unwanted sounds can be discarded automatically, without any manual analysis or editing. Our API acts as an assistant, finding and discounting irrelevant sounds from reports. It also sorts these sounds into daytime and nighttime categories to buid an effective audio report.
Audio Labelling: Detection of Noise Events
Audio labelling involves identifying specific events or sounds in the audio recording and labelling them accordingly. This could include, for example, identifying when a vehicle passes by, when construction work begins or ends, when the sound of a drill exceeds a certain decibel level or even when a dog barks. These labels can then be used to classify different types of noise and to track noise levels over time.
Audio Feature Extraction: Processing
Audio feature extraction involves extracting numerical features from the audio signal that can be used to describe its characteristics. This might include measurements of the frequency spectrum, the duration of specific sounds, or the loudness of the signal. These features can then be used to identify patterns in the data and to make predictions about future events.In the case of the noise consultancy firm, their web application uses AI to automatically exclude periods in the wave file with background noise, identify maximum noise events, and automatically tag the source of each noise event
One of the most significant benefits of using AI to analyze audio data is the ability to automate the reporting process. Traditionally, creating a report from audio data was a time-consuming and tedious task that required a skilled noise analyst. However, with the power of AI, this task can now be completed quickly and efficiently.Auto-reporting is the process of automatically generating a report from analysed audio data. Once the API has detected, tagged, and analyzed the audio data, it creates a report in the form of a PDF. The report contains all the necessary information about the audio exhibited, making it easier for noise analysts to do their job more efficiently
Automated audio analysis is a powerful tool for noise analysts, allowing them to save time and improve the accuracy of their work. By using AI and machine learning techniques to automatically analyse audio data, noise analysts can focus on higher-level tasks like data interpretation and decision-making. Whether you’re a noise analyst or a researcher in another field, the benefits of audio analysis are clear. By breaking down audio signals into their individual components, you can gain a better understanding of the world around you and make more informed decisions as a result.
If you want to experience the power of our innovative technology for yourself, we invite you to try it out with our free trial.
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By Johanna Walsh