The very first thing to try would be to implement a rough speech/music classifier to detect a radio host/DJ; and to extract the amplitude envelope to detect fade outs and fade ins.
Once you have implemented that and/or if this doesn't solve your problem because your stream is continuous (DJ-style mix), you'll certainly run into the song boundary / song section boundary ambiguity - but if this is not a problem to you then there are solutions. What you are looking for is known in the academic community as ``unsupervised audio stream segmentation" - with a variety of techniques proposed over the years. You have to decide on two ingredients: which audio features to extract from the audio stream; and which statistical analysis method to use to detect changes/structure.
Regarding the features, the consensus is on a combinations of the following:
- MFCC (which carry timbre/instrumentation information)
- Chroma (which carry key/chord information)
- Tempo or other measures of rhythm (beat histogram, Pampalk's fluctuation patterns, etc.)
Very frequently, an algorithm can be repurposed just by a change of features... For example, there are papers on musicological analysis of piano music which will not use MFCC (same timbre throughout the piece), or papers on pop song structure analysis which will not use tempo (assumed to be constant throughout the piece, so not informative) - but the methods they describe could very well work for you if you decide to include other features - so don't be narrow when looking for literature.
Regarding the statistical analysis methods, the usual suspects are:
Computation of a self-similarity matrix, and "checkerboard" filter to detect blocks. The classic paper is by Foote, "Automatic Audio Segmentation Using A Measure of Audio Novelty". Foote's novelty detection function can be computed online and incrementally - over a buffer of past audio frames.
Hidden Markov models, used in an unsupervised way. You hope that the HMM states will map well into segments (Peeters and Rodet, "Music Structure Discovering Using Dynamic Audio Features for Audio Summary Generation: Sequence and State Approach"), or you overestimate the number of HMM states and look for changes in the HMM state distribution within small windows (Levy and Sandler, "Structural Segmentation of Musical Audio by Constrained Clustering"). These methods are more geared towards offline processing, and situations in which you have an idea in advance of how many segments you want to identify.
"Streaming" change detection methods. The principle is to split the past $N$ frames of audio into two $N/2$ frames blocks $S_1$ and $S_2$, and to perform a statistical test to decide which of the hypotheses "Frames from $S_1$ and $S_2$ are drawn from the same distribution $P$" and "Frames from $S_1$ and $S_2$ are drawn from a different distribution $P_1$ and $P_2$" is the most likely. Big peaks or dips in the test outcome indicate that the middle of the block is a segment change. There are a number of information criteria used to weigh the two hypotheses, such as the BIC (Classic paper: "Unsupervised Audio Stream Segmentation And Clustering Via The Bayesian Information Criterion" by Zhou). The limitation here is that you usually have little data to perform the test, and the data has weird-looking distributions. See the section 2.2 of Gillet et al's "Comparing audio and video segmentations for music videos indexing" for a few pointers on kernel methods that overcome these limitations.
For a real-life application (indexing of radio streams), see for example Ramona's "Combined supervised and unsupervised approaches for automatic segmentation of radiophonic audio streams" - this work combined the kind of unsupervised section detectors discussed here to a supervised speech/music classifier (to tackle the most obvious part of the problem).