Introduction to Audio Analysis

As a sound engineer, your primary role is to capture, manipulate, and reproduce sound to achieve the desired audio quality. To excel in this field, a solid understanding of audio analysis is indispensable. Audio analysis encompasses various techniques and tools used to examine, measure, and interpret audio signals. Our aim here is to provide an introductory overview of audio analysis, focusing on key concepts, tools, and techniques crucial for sound engineers.

Audio analysis serves multiple purposes in sound engineering. It helps in identifying and resolving technical issues, optimizing acoustic environments, enhancing sound quality, and ensuring consistency across different platforms and mediums. By analyzing audio signals, engineers can make informed decisions about equalization, compression, noise reduction, and other processing tasks.

Fundamental Concepts in Audio Analysis

Any signal can be either seen from its evolution in time or from its frequency content. An analyzer tries to extract relevant data from an audio stream to turn it into a meaningful visual representation. This leads to the two major families of analyses one can perform on an audio signal:

Frequency Spectrum

The frequency spectrum represents the distribution of energy across different frequencies in an audio signal. Tools like Real-Time Analyzers (RTAs) and spectrograms are commonly used for this purpose.

In a conventional digital system, audio material is captured, stored, transmitted, and reproduced as a sequence of values, which correspond to the amplitude variations of an electric signal at discrete points in time. Our ability to extract meaningful information from this raw data through either hearing or visualization of the signal curve is somewhat limited to emotional interpretation, which is extremely subjective.

Extensive studies have shown that first converting this data to a so-called frequency representation is extremely useful for a broad range of audio applications, as it is quite similar in principle to the human auditory system. A proper detailed explanation of the reasons behind this is well outside of the scope of this manual, so we will only hint at a few important characteristics of human hearing, namely its:

• Ability to recognize and isolate sounds based on their relative intensity or loudness
• Ability to identify a pitch and timbre (color, texture) for sounds that fall in this category
• Ability to distinguish sounds based on their actual or perceived location

A fundamental tool for transforming a time-based digital audio signal into a frequency-based representation, a.k.a frequency spectrum, is the discrete Fourier transform (DFT) and its derivatives, such as the Short-Term Fourier Transform (STFT) and Fast Fourier Transform (FFT). Basically, the DFT maps a signal to a set of amplitudes taken at equally spaced frequency intervals. In essence, one can see the DFT as a bank of many band-pass filters, with as many meters at the output of these filters.

Level Analysis

Level analysis is a fundamental aspect of audio signal evaluation, focusing on the measurement and monitoring of signal amplitude over time. This process involves tracking various level metrics, such as peak, RMS (Root Mean Square), and loudness units, to ensure optimal dynamic range and prevent distortion. Sound engineers use level meters to visualize these metrics, enabling them to make informed decisions about gain staging, compression, and limiting. Oscilloscopes and waveform analysis can also give some significant insight into distortions that may have happened on the signal.

MiRA: Advanced Audio Analysis Suite

MiRA equips sound engineers with a comprehensive range of real-time audio analysis tools designed to streamline workflows and enhance output quality. At the core of these tools lies FLUX’s proprietary Variable Q Transform algorithm, which outperforms the classic FFT by offering both reduced computational load and superior data readability.

MiRA features industry-leading spectrum analyzers, spectrograms, true peak/RMS/loudness meters, oscilloscopes, and vectorscopes. The application also allows users to customize their workspace by arranging these tools to suit their specific needs. Each tool offers extensive settings for further personalization.

A unique feature of MiRA is its spatial spectrogram, a powerful tool designed to analyze the spatial characteristics of audio signals. This sophisticated tool generates a detailed map of the soundscape, enabling engineers to understand and manipulate the spatial distribution of audio elements with ease. This capability is invaluable for crafting dynamic and engaging audio environments, ultimately providing listeners with a more immersive experience.

Understanding Audio Signal Chains and the Role of Measurement Tools

At first glance, an audio signal chain is very much like a series of black boxes. As an audio engineer, you can trust your ears and the manufacturer’s data sheets to assess the effects this chain has on the incoming audio. In a variety of cases, however, this is either simply impractical, not possible, or not precise enough. Such situations include live sound setups, recording setups, etc., where unknown factors, such as the venue’s or studio’s acoustic response, are a crucial part of the chain.

It is therefore necessary to resort to scientific measurement procedures and tools to obtain precise, trustworthy, and reproducible results. The main tools at your disposal for this purpose are transferring curve and impulse response measurements, which are especially designed for this task.

As with any measurement instrument, it is important to have a good grasp of its mode of operation as well as any possible limitations in order to use it most efficiently. Some knowledge of acoustic principles and notions of signal processing are naturally required as well. While this manual tries to cover the most typical use cases and points out common do’s and don’ts, it obviously cannot replace either a good textbook or practical experience.