Introduction to the Acoustic Diversity Index (ADI)

Over the last three years of maintaining this blog, there’s been a conspicuous absence in the acoustic index toolkit presented so far. Whilst the Bioacoustic Index (BI), Acoustic Complexity Index (ACI) and Normalised Difference Soundscape Index (NDSI) have been introduced and demonstrated across given case studies, the Acoustic Diversity Index (ADI) hasn’t been covered to date.

Why is this? When I first started applying acoustic indices a few years ago, I could never find the right fit for the ADI, and in fact I don’t think I really understood how it could be applied properly. In this respect, I would regard it now as one of the more fussy ecoacoustic metrics which requires more forethought and care when setting its parameters and reviewing the output.

It also prompted me to go back to basics. If you’ve been checking out this blog or my YouTube channel lately, you will have seen some recent videos which have been created to provide simple, introductory overviews of ecoacoustic metrics. Sometimes it’s useful to go back to the fundamentals (even in the case of the well-covered ACI) and it’s also handy to have a video overview that can be embedded in a post.

The Acoustic Diversity Index

The development of the ADI is based on the observation that ‘acoustic community diversity is the aggregation of all species that produce sounds at a particular location and time.’ (Bobryk et al., 2016, Sueur et al., 2014) and that ‘each community has a distinctive acoustic signature depending on species communication’ (Farina & Pieretti, 2014.)

There’s a clear distinction here between the ADI and ACI, which – based on my earlier attempts to apply ADI – I hadn’t been able to fully grasp.

The ADI is primarily concerned with how much diversity (at a given intensity threshold) is present across the frequency spectrum, whereas the ACI is primarily concerned with a distinction between transient and continuous acoustic events.

An example of a transient event might be bird or frog vocalisation, whereas continuous events could be insect trill, the cascade of a creek or human-generated sounds like machinery or vehicles.

Another way of considering this distinction is that the ACI is concerned primarily with events occurring horizontally (over time), whereas the ADI is concerned primarily with events occurring vertically (across the spectrum, irrespective of an event’s duration.)

Now, there’s much more to it than that, but in principle, and where the ADI is concerned, if you have lots of different sounds occurring across the frequency spectrum which are loud enough, this should produce a high ADI value.

How the ADI computes its metric

Below we have a high-level abstraction of the ADI process for a given audio file. The file in this instance is a one-minute recording of a dawn chorus. As we can see, there’s a lot of activity occurring across the frequency spectrum, so we can assume that this will likely return a high ADI value.

The frequency spectrum is analysed and discretised into frequency bins against time samples. From here, the ADI algorithm computes the amount of diversity present across these bins.

An ADI value of 3.67 is returned. The ADI metric ranges from 0.01 (very low) to 5.0 (very high), so we can conclude that we have reasonably high acoustic diversity in this recording.

Executing ADI for this single recording in R requires the soundecology, seewave and tuneR packages. I’ve also created some custom functions and instantiated the built-in package functions. meanspec() will create a visualisation of the averaged spectral analysis, fbands() will discretise the spectral analysis into bands (10 are defined in this case) and acoustic_diversity() will perform the ADI analysis on the audio file.

The code for this post and audio files can be found at the relevant GitHub repository here

library(soundecology)
library(seewave)
library(tuneR)

# create functions to perform spectral analysis and compute ADI --
# 1. spectral_viz: spectral analysis of given file
spectral_viz <- function(file, range) {
  meanspec(file, f=range, plot=T)
}

# 2. discrete: discretise spectral analysis into frequency bins --
discrete <- function(spectral_analysis) {
  fbands(spectral_analysis, bands=10)
}

# 3. compute the ADI for given soundfile -- 
compute_adi <- function(file) {
  acoustic_diversity(file, max_freq = 10000, db_threshold = -20, 
                     freq_step = 50, shannon = TRUE)
}

# create the spectral analysis, bins and perform ADI --

spec.dw <- spectral_viz(dawn, 16000)
discrete(spec.dw)
compute_adi(dawn) # Acoustic Diversity Index: 3.670878

Before moving on, let’s look at an example where the acoustic diversity is very low.

Here we have a recording made on the banks of American River in Kangaroo Island. It was a still afternoon with very little acoustic activity, evidenced by the fairly barren spectrogram below. We can see that there’s a little intensity occupying the lower end of the spectrum (distant boats) and a couple of transient events (birdsong) in the latter section of the recording.

As a spectral analysis these couple of transient events can’t compete with the dominant activity in the lower end and therefore the ADI once computed is very low at 0.01.

If you’d like to listen to these and other standalone recordings here’s the overview video I’ve created. The time marks for the sections are listed below:

• 3:27 – Dawn chorus ADI analysis
• 4:35 – American River ADI analysis
• 5:48 – Rocky River (Kangaroo Island) ADI analysis
• 6:16 – Hay Flat Road, Normanville ADI analysis
• 6:43 – Mu Ko Lanta National Park, Thailand ADI analysis
• 7:12 – Adelaide Central Markets ADI analysis
• 7:40 – Bungaree Station dawn chorus ADI analysis

Current challenges for ADI analysis

Whilst the other acoustic indices I’ve covered to date (BI, ACI, NDSI) can deal with acoustic data containing biases such as the dominance of natural sounds (such as river cascades), the influence of weather (wind, rain) and device self-noise, the ADI appears to be a lot more sensitive to this.

I believe that one of the reasons for this is due to the previously mentioned vertical/horizontal distinction associated with the ADI and ACI.

The ‘vertical’ spectral reading of the ADI does not place as much importance on the ‘horizontal’, time-based states of acoustic events. For reference, here’s that previous graphic that I shared in the introduction.

Depending on an interaction with the recording input and its enclosure, biases like weather and self-noise typically encompass a lot of the frequency spectrum. These are pretty ‘rough’ and noisy sounds after all. And since the duration of an event isn’t important to how the ADI is reading the data, the time-based (transient or continuous) attributes of acoustic events are largely irrelevant.

Because of this, the ADI is partial to interpreting these events as being highly diverse since they may encompass a big chunk of the spectrum and be regarded as sounds from several sources when they may only be attributed to one!

The following example of two recordings made at the same site underscores this problem (see below.)

The first recording has a sole key attribute, which is the sound of the creek cascade. As it encompasses several areas of spectrum and is consistent, the ADI is assuming that it of a high acoustic diversity, resulting in an ADI value of 3.26.

Recall the earlier example of a dawn chorus which also returned a high ADI value (3.67.)

Now, a creek cascade can be acoustically diverse when considered in isolation, but this fundamentally betrays the purpose of the ADI which is intended to measure ‘community diversity’. Within the context of the ADI, the creek recording isn’t actually diverse, whereas the dawn chorus (consisting of various bird species) is diverse.

So this becomes especially problematic when this first recording is measured against another recording from the same site which is – on a cursory listen – genuinely diverse.

The second recording again features the creek’s cascade, but also the sound of birds, frogs and crickets. When ADI is computed, it returns an ADI of 1.07! This is more than 2 points lower than the creek cascade recording.

So this isn’t an accurate representation of the acoustic diversity at this site and this presents a huge challenge when ADI analysis is scaled out to measure acoustic diversity at one site over an extended period.

How do we resolve this? I have some solutions in mind, but I’m going to carry this over to another instalment in the future.

In the meantime, I hope you’ve gotten something out of this introduction to the ADI. As always, if you have any questions or feedback, please leave a comment below the post or get in touch with me directly.