Parrots are so adept at mimicking people that the avian moniker has become synonymous with repetition. Yet for as long as we've known about the birds' incredible ability for impressions, how they manage such complex and flexible vocalizations has been a mystery. A new study offers a piece of the puzzle by peeking into the parakeet brain, and finds remarkable similarities to the human neural region that controls speech. The research, published March 19 in the journal Nature, suggests parrots (and specifically parakeets) could be a model for studying human speech, helping scientists to better understand and treat speech disorders. It also adds to the growing stack of scientific findings that demonstrate "bird-brained" isn't much of an insult after all.

We propose a method for accurately detecting bioacoustic sound events that is robust to overlapping events, a common issue in domains such as ethology, ecology and conservation. While standard methods employ a frame-based, multi-label approach, we introduce an onset-based detection method which we name Voxaboxen. It takes inspiration from object detection methods in computer vision, but simultaneously takes advantage of recent advances in self-supervised audio encoders. For each time window, Voxaboxen predicts whether it contains the start of a vocalization and how long the vocalization is. It also does the same in reverse, predicting whether each window contains the end of a vocalization, and how long ago it started. The two resulting sets of bounding boxes are then fused using a graph-matching algorithm. We also release a new dataset designed to measure performance on detecting overlapping vocalizations. This consists of recordings of zebra finches annotated with temporally-strong labels and showing frequent overlaps. We test Voxaboxen on seven existing data sets and on our new data set. We compare Voxaboxen to natural baselines and existing sound event detection methods and demonstrate SotA results. Further experiments show that improvements are robust to frequent vocalization overlap.

Signals in the brains of birds have been read by scientists, in a breakthrough that could help develop prostheses for humans who have lost the ability to speak. In the study silicon implants recorded the firing of brain cells as male adult zebra finches went through their full repertoire of songs. Feeding the brain signals through artificial intelligence allowed the team from the University of California San Diego to predict what the birds would sing next. The breakthrough opens the door to new devices that could be used to turn the thoughts of people unable to speak, into real, spoken words for the first time. Current state-of-the-art implants allow the user to generate text at a speed of about 20 words per minute, but this technique could allow for a fully natural'new voice'.

New research demonstrates for the first time that artificial intelligence (AI) can be used to train computers to recognize individual birds, a task humans are unable to do. The research is published in the British Ecological Society journal Methods in Ecology and Evolution. "We show that computers can consistently recognize dozens of individual birds, even though we cannot ourselves tell these individuals apart. In doing so, our study provides the means of overcoming one of the greatest limitations in the study of wild birds--reliably recognizing individuals." Said Dr. André Ferreira at the Center for Functional and Evolutionary Ecology (CEFE), France, and lead author of the study.

An immense frustration ecologists encounter is prompted by the attempt to keep track of individual animals in a study. This task only becomes more difficult when trying to pinpoint small, mobile animals like songbirds. While intelligent computer algorithms can help scientists better complete this task, training these systems to recognize different species -- let alone individuals in a species -- can take thousands of data points, time, and money. However, French and Portuguese researchers recently devised a way to streamline this process. They designed a deep-learning network that can identify individual birds with up to 92 percent accuracy in three different species. This tech can not only save scientists resources but can help them collect important data about the lives of birds -- and better understand what may be leading to their decline in North America.

Distinguishing between individual animals is important for long-term monitoring of populations and protecting species from pressures such as climate change. However, it is also one of the most expensive, troublesome, and time-consuming aspects of animal behaviour research. While some creatures such as leopards have unique markings which allow humans to recognise individuals by eye, most species require additional visual identifiers such as coloured bands to be distinguished. Attaching bands to birds' legs can be stressful and disruptive to the animals, limiting the scope of research. Seeking an alternative method for distinguishing between individual birds, researchers from institutes in France, Germany, Portugal, and South Africa developed the first AI bird identification tool of its kind.

Trying to identify a wild bird while frantically leafing through a bird-spotters' guide is no easy task. But modern technology has come to the rescue, with artificial intelligence trained to help out amateur twitchers. Where people may be confused by two similar looking birds, or a juvenile which does not yet have its adult plumage, AI has been found to identify birds with up to around 90 per cent accuracy. The technology was trained using pictures of wild great tits and sociable weavers, as well as captive zebra finches. It works in a similar way to the face-recognition programmes used to identify people in crowds.

We are all born with a predisposition for music, a predisposition that develops spontaneously and is refined by listening to music. Nearly everyone possesses the musical skills essential to experiencing and appreciating music. Think of "relative pitch,"recognizing a melody separately from the exact pitch or tempo at which it is sung, and "beat perception,"hearing regularity in a varying rhythm. Even human newborns turn out to be sensitive to intonation or melody, rhythm, and the dynamics of the noise in their surroundings. Everything suggests that human biology is already primed for music at birth with respect to both the perception and enjoyment of listening. Human musicality is clearly special. Musicality being a set of natural, spontaneously developing traits based on, or constrained by, our cognitive abilities (attention, memory, expectation) and our biological predisposition.

Entrepreneurs in Silicon Valley this year set themselves an audacious new goal: creating a brain-reading device that would allow people to effortlessly send texts with their thoughts. In April, Elon Musk announced a secretive new brain-interface company called Neuralink. Days later, Facebook CEO Mark Zuckerberg declared that "direct brain interfaces [are] going to, eventually, let you communicate only with your mind." The company says it has 60 engineers working on the problem. It's an ambitious quest--and there are reasons to think it won't happen anytime soon.

Human babies seem to have a natural knack for languages. Fluency in any of the world's 6,500 languages comes within the first few years of life, without much apparent effort. A recent study of how songbirds learn their melodies seeks to shine light on the cognitive processes through which young birds learn and imitate vocal communication – insights which could lead to a better understanding of the development of human speech, as well. "A bird's baby song is really immature. There's no clear structure - it's more like a baby babbling, but then it becomes structured, like the tutor song [as the bird gets older]," Yoko Yazaki-Sugiyama, co-author of the study published in Nature Communications Tuesday, tells The Christian Science Monitor in a phone interview. The male zebra finch learns a complex song from his father, or tutor, in order to attract a female finch.